ptzagk / data_science

daily curated links in DS, DL, NLP, ML

data_science

seeing is believing. A witty saying proves nothing.

"When solving a problem of interest, do not solve a more general problem as an intermediate step." (Vladimir Vapnik)

Winining solutions

• http://ndres.me/kaggle-past-solutions/
• Rossmann Sales Forecasting, 1st solution: https://kaggle2.blob.core.windows.net/forum-message-attachments/102102/3454/Rossmann_nr1_doc.pdf

Case stydies:

• auc, https://www.kaggle.com/c/acquire-valued-shoppers-challenge#evaluation
• auc, https://www.kaggle.com/c/kdd-cup-2014-predicting-excitement-at-donors-choose#description

DS Coursera

• http://www.chioka.in/how-to-select-your-final-models-in-a-kaggle-competitio/
• http://scikit-learn.org/stable/modules/cross_validation.html

Heroes of DL

• Geoffrey Hinton: https://www.youtube.com/watch?v=-eyhCTvrEtE
• Andreij Karpathy: https://www.youtube.com/watch?v=_au3yw46lcg

Top conferences:

• KDD 2018 London, UK: http://www3.imperial.ac.uk/newsandeventspggrp/imperialcollege/engineering/datascienceinstitute/newssummary/news_22-8-2017-11-17-28
• WSDM 2018, US: http://www.wsdm-conference.org/2018/
• NIPS 2017, Long Beach, US: https://nips.cc/
• DepLing 2017: http://www.depling.org/depling2017/program.html
• CIKM 2017: http://cikm2017.org/
• https://webdocs.cs.ualberta.ca/~zaiane/htmldocs/ConfRanking.html
• http://www.guide2research.com/topconf/machine-learning
• http://portal.core.edu.au/conf-ranks/?search=&by=all&source=CORE2017&sort=atitle&page=1

Deep Learning

• http://www.deeplearningbook.org/contents/applications.html

Events: I will put word cloud for that.

EMNLP 2017: http://noisy-text.github.io/2017/

NLPStan reading

• http://nlp.stanford.edu/read/
• NLP dataset: https://github.com/niderhoff/nlp-datasets

LXMLS16:

• http://lxmls.it.pt/2016/Deep-Neural-Networks-Are-Our-Friends.pdf
• http://lxmls.it.pt/2016/lxmls-dl2.pdf

ACL2017

• keynote: linguistic is back, reduce search space: https://drive.google.com/file/d/0B2cCJQ2_aOwjMlg5MnFjTEpBNG8/view

VietAI

• Quoc Le (Google Brain): http://cs.stanford.edu/~quocle/
• Thang Luong (Google Brain): http://t.co/3zNHouUn
• Dustin (Columbia) http://dustintran.com/
• Thien (NYU) http://www.cs.nyu.edu/~thien/
• Hieu Pham (CMU) https://www.quora.com/profile/Hieu-Pham-20
• Ken Tran (Microsofts) http://www.kentran.net/
• Laurent Dinh (MILA):https://laurent-dinh.github.io/about/
• Luong Hoang, Harvard: https://github.com/lhoang29/recurrent-entity-networks
• Vu Pham

My SOTA

• My ATIS: sequence tagging, nb of params: 324335, bi-LSTM
• Quore question duplicate detection: Accuracy 85% on Wang's test

 - best F1 score: 94.92/94.64
 - train scores: 97.5446666667/96.17
 - val scores: 93.664/92.94

Yandex

• https://github.com/ddtm/dl-course
• https://github.com/vkantor/MIPT_Data_Mining_In_Action_2016/tree/master/trends
• https://github.com/yandexdataschool/Practical_RL
• https://github.com/yandexdataschool/HSE_deeplearning

ICLR 2017 Review

• if you wanna turn LSTM, it's worth to read (from Socher): https://arxiv.org/pdf/1611.05104v2.pdf

LearningNewThingIn2017

• Torch/Lua (Facebook/HarvardNLP): http://nlp.seas.harvard.edu/code/, http://cs287.fas.harvard.edu/
• TF/Python (Google/Stanford): https://github.com/BinRoot/TensorFlow-Book
• cs287: https://github.com/CS287/Lectures

Conf events

• Coling 2016, Osaka Japan: http://coling2016.anlp.jp/
• ICLR 2017, Apr in France: http://www.iclr.cc/doku.php?id=ICLR2017:main&redirect=1
• open review: http://openreview.net/group?id=ICLR.cc/2017/conference

NIPs 2016 slides

• https://github.com/hindupuravinash/nips2016
• Ian GAN tut: http://www.iangoodfellow.com/slides/2016-12-9-gans.pdf
• Ng nuts and bolts: https://www.dropbox.com/s/dyjdq1prjbs8pmc/NIPS2016%20-%20Pages%202-6%20(1).pdf
• variational inference: http://www.cs.columbia.edu/~blei/talks/2016_NIPS_VI_tutorial.pdf

Theano based DL applications

• https://news.ycombinator.com/item?id=9283105

learn to learn: algos optimization

• sgd and friends: http://cs231n.github.io/neural-networks-3/#update
• overview of gd: http://sebastianruder.com/optimizing-gradient-descent/
• keras-team/keras#898
• I used to choose adam and rmsprop with tuning lr and batch size.

Pin:

• semantic scholar: https://www.semanticscholar.org/
• grow a mind: http://web.mit.edu/cocosci/Papers/tkgg-science11-reprint.pdf
• trendingarxiv: http://trendingarxiv.smerity.com/
• https://github.com/andrewt3000/DL4NLP
• Natural languague inference NLI: https://github.com/Smerity/keras_snli
• ACL: http://www.aclweb.org/anthology/P/P16/

Data type: NOQ

• Nominal (N):cat, dog --> x,o | vis: shape, color
• Ordinal (O): Jan - Feb - Mar - Apr | vis: area, density
• Quantitative (Q): numerical 0.42, 0.58 | vis: length, position

People:

• Graham CMU: http://www.phontron.com/teaching.php, https://github.com/neubig/

Fin data:

• Reuters 8M (2007-2016): https://github.com/philipperemy/Reuters-full-data-set.git
• Bloomberg https://github.com/philipperemy/financial-news-dataset
• stocktwits: https://github.com/goodwillyoga/E107project/tree/master/pooja/data

Projects:

• https://github.com/THEdavehogue/glassdoor-analysis

Wikidata:

• https://github.com/VladimirAlexiev/VladimirAlexiev.github.io/blob/master/CH-names/README.org
• https://github.com/VladimirAlexiev/VladimirAlexiev.github.io/tree/master/CH-names

Cartoons & Quotes:

• "cause you know sometimes words have two meanings" led zeppelin
• http://stats.stackexchange.com/questions/423/what-is-your-favorite-data-analysis-cartoon?newsletter=1&nlcode=231076%7C1179

Books:

• http://neuralnetworksanddeeplearning.com/index.html
• u.cs.biu.ac.il/~yogo/nnlp.pdf

Done:

1. EMNLP 2016, Austin, 2-4 Nov: http://www.emnlp2016.net/tutorials.html#practical

• Dynet (CMU: https://t.co/nSCkBt0i0F

• lifelong ML (Google): http://www.emnlp2016.net/tutorials/chen-liu-t3.pdf

• Markov logic for scalable joint inference: http://www.emnlp2016.net/tutorials/venugopal-gogate-ng-t2.pdf

• good summary of sentiment analysis with NN (Singapore): http://www.emnlp2016.net/tutorials/zhang-vo-t4.pdf

• structure prediction (POS, NER)(Singapore): http://www.emnlp2016.net/tutorials/sun-feng-t6.pdf

• BADLS: 2 day conference at Stanford university

day 1:

• Hugo(Twitter): Feed forward NN
• Kartpathy(OpenAI): Convnet
• Socher(MetaMind): NLP = word2vec/glove + GRU + MemNet
• Tensorflow tut: from 5:55:49
• Ruslan: Deep Unsup Learning: from 7:10:39
• Andrew Ng: Nuts and bolts in applied DL from 9:09:46

day 2:

• Schulman: RL from 06:40

• Pascal(MILA): theano, from 1:52:03

• ASR from 4:01:11

• NN with Torch from 5:49:32, https://github.com/alexbw/bayarea-dl-summerschool

• seq2seq learning, Quoc Le: from 7:03:44

• Bengio: Foundations and challenges in DL, from 9:01:14

• data fest: https://alexanderdyakonov.wordpress.com/

• 8,9,12,13 Sept: data science week: http://dsw2016.datascienceweek.com/

• KDD 2016: http://www.kdd.org/kdd2016/

• ACL 2016, Berlin, 7-12 Aug: http://acl2016.org/index.php?article_id=60

AI mistakes:

• napalm girl: https://techcrunch.com/2016/09/12/facebook-employees-say-deleting-napalm-girl-photo-was-a-mistake/
• fine for his car shadow: http://www.independent.co.uk/news/world/europe/russian-driver-fined-car-shadow-moscow-a7225146.html
• human on motorcycle: http://cs.stanford.edu/people/karpathy/deepimagesent/generationdemo/

Keras:

• image classification with vgg16: http://www.pyimagesearch.com/2016/08/10/imagenet-classification-with-python-and-keras/
• hualos, keras viz: https://github.com/fchollet/hualos
• https://github.com/dylandrover/keras_tutorial/blob/master/keras_tutorial/keras_deck.pdf
• https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/learn/wide_n_deep_tutorial.py
• model zoo:https://github.com/tensorflow/models
• music auto tag: https://github.com/keunwoochoi/music-auto_tagging-keras
• expose API: https://github.com/samjabrahams/inception-resnet-flask-demo

NLP:

• https://github.com/attardi/deepnl
• https://github.com/biplab-iitb/practNLPTools
• http://ml.nec-labs.com/senna/
• LSTM + CNN char on NER: https://transacl.org/ojs/index.php/tacl/article/viewFile/792/202
• https://metamind.io/research/the-wikitext-long-term-dependency-language-modeling-dataset/

Apps:

• https://github.com/fginter/w2v_demo
• http://bionlp-www.utu.fi/wv_demo/
• 3top: https://github.com/3Top/word2vec-api
• next wave of nn: http://www.nextplatform.com/2016/09/14/next-wave-deep-learning-applications/
• labeling tools: http://cs.stanford.edu/people/karpathy/ilsvrc/
• deep art: https://deepart.io/hire/kzXhuUPf/
• text sum: http://esapi.intellexer.com/Summarizer
• http://www.deeplearningpatterns.com/doku.php/applications
• mt: http://104.131.78.120/
• rnn: http://www.cs.toronto.edu/~ilya/fourth.cgi?prefix=I+have+a+dream.+&numChars=150
• chatbot: http://sumve.com/firesidechat/
• text vis: http://slanglab.cs.umass.edu/topic-animator/
• music auto tag: https://github.com/keunwoochoi/music-auto_tagging-keras
• deep image sent: http://cs.stanford.edu/people/karpathy/deepimagesent/rankingdemo/

German word embedding:

• pretrained: http://devmount.github.io/GermanWordEmbeddings/
• vis: pca, tsne: https://github.com/devmount/GermanWordEmbeddings/blob/master/code/pca.ipynb

PyGotham:

• textacy: http://michelleful.github.io/code-blog/2016/07/23/nlp-at-pygotham-2016/
• nlp with keras, rnn, cnn
• https://github.com/drincruz/PyGotham-2016
• skipthought: https://libraries.io/github/LeavesBreathe/Sequence-To-Sequence-Generation-Skip-Thoughts-
• https://github.com/ryankiros/skip-thoughts
• doc sum: http://mike.place/talks/pygotham/#p1

Journalist LDA and ML:

• http://knightlab.northwestern.edu/2015/03/10/nicar-2015-machine-learning-lessons-for-journalists/
• summary on hanna wallach https://docs.google.com/document/d/1kIIzBAF9T9Zu99i0DU9akIajvYZ-CfHeBFVBhIJyEY8/edit?pref=2&pli=1
• http://www.cs.ubc.ca/~murphyk/MLbook/pml-toc-22may12.pdf
• http://slides.com/stevenrich/machine-learning#/18
• https://github.com/cjdd3b/nicar2015/tree/master/machine-learning
• https://github.com/cjdd3b/fec-standardizer

Europython:

• http://kjamistan.com/i-hate-you-nlp/
• https://github.com/adewes/machine-learning-chinese
• https://github.com/GaelVaroquaux/my_topics
• https://github.com/arnicas/nlp_elasticsearch_reviews

Scipy 2016:

• http://scipy2016.scipy.org/ehome/146062/332963/

Performance Evaluation(PE):

• book ELA: http://www.cambridge.org/us/academic/subjects/computer-science/pattern-recognition-and-machine-learning/evaluating-learning-algorithms-classification-perspective
• slides: http://www.icmla-conference.org/icmla11/PE_Tutorial.pdf
• bayesian hypothesis testing: http://ipg.idsia.ch/preprints/corani2015c.pdf

Hypothesis testing

• http://bebi103.caltech.edu/2015/tutorials/t6b_frequentist_hypothesis_testing.html
• central limit theorem: http://nbviewer.jupyter.org/github/mbakker7/exploratory_computing_with_python/blob/master/notebook_s3/py_exp_comp_s3_sol.ipynb
• hypothesis testing and p value: http://vietsciences.free.fr/khaocuu/nguyenvantuan/bieudoR/ch7-kiemdinhgiathiet.htm

Metrics:

• http://users.dsic.upv.es/~dpinto/duc/RougeLin.pdf

Rock, Metal and NLP:

• http://www.deepmetal.io/
• https://github.com/ijmbarr/metal_models
• http://www.degeneratestate.org/posts/2016/Sep/12/heavy-metal-and-natural-language-processing-part-2/
• http://www.degeneratestate.org/posts/2016/Apr/20/heavy-metal-and-natural-language-processing-part-1/

Financial:

• https://github.com/johnymontana/NewzTrader_AI_project

Twitter:

• http://nlp.stanford.edu/projects/glove/preprocess-twitter.rb
• GATE NER dataset: https://gate.ac.uk/wiki/broad-twitter-corpus.html

Deep Learning Frameworks/Toolkits:

• Tensorflow
• Torch
• Theano
• Keras
• Dynet
• CNTK

ElasticSearch + Kibana:

• install ES 2.4 + Kibana: default sense in console 5601
• http://ghostweather.slides.com/lynncherny/deck

Attention based:

• code RWA in TF: https://github.com/jostmey/rwa
• decomposable attention: https://github.com/explosion/spaCy/tree/master/examples/keras_parikh_entailment
• customized lstm with attention: http://benjaminbolte.com/blog/2016/keras-language-modeling.html
• vis + cnn + lstm: https://blog.heuritech.com/2016/01/20/attention-mechanism/

ResNet: Residual Networks

• http://yanran.li/peppypapers/2016/01/10/highway-networks-and-deep-residual-networks.html
• how deep Vgg 16,19 vs 152 200 layers: https://www.reddit.com/r/MachineLearning/comments/4cmcfs/how_can_resnet_cnn_go_deep_to_152_layers_and_200/
• http://www.slideshare.net/Textkernel/practical-deep-learning-for-nlp

Sentiment

• dataset: 1.6M: https://docs.google.com/uc?id=0B04GJPshIjmPRnZManQwWEdTZjg&export=download
• quandl: https://github.com/kszela24/options-daily
• stocktwit: http://stocktwits.com/symbol/FINL
• https://github.com/jssandh2/Stock_Search_Engine
• https://www.quantopian.com/posts/crowd-sourced-stock-sentiment-using-stocktwits
• https://www.crowdflower.com/data-for-everyone/

NER

• https://github.com/aleju/ner-crf
• 2017 conference: http://noisy-text.github.io/2017/
• demo: http://nlp.stanford.edu:8080/ner/process
• ritter: https://www.cise.ufl.edu/class/cis6930fa11lad/cis6930fa11_NEROverTweets.pdf
• cmu tweetnlp: http://www.cs.cmu.edu/~ark/TweetNLP/
• opencalais: http://www.opencalais.com/opencalais-demo/
• https://www.quora.com/How-can-I-find-city-country-company-name-from-a-tweet-text-using-Java
• no broad domain, average accuracy 80-85% is quite good: https://www.quora.com/How-accurate-are-entity-extraction-tools
• http://blog.districtdatalabs.com/named-entity-recognition-and-classification-for-entity-extraction
• http://noisy-text.github.io/2016/ner-shared-task.html
• https://noisy-text.github.io/2016/pdf/WNUT26.pdf
• dataset: https://www.dropbox.com/s/yaoy7zi9vz71nki/wnut_ner_evaluation.tgz?dl=0
• wnut solution: https://github.com/napsternxg/TwitterNER
• dataset wnut16: https://github.com/aritter/twitter_nlp/tree/master/data/annotated/wnut16/data

ML Stacking

• brew: https://github.com/viisar/brew
• heamy: https://github.com/rushter/heamy

Tensorflow tutorials

• https://github.com/alrojo/tensorflow-tutorial
• https://github.com/farizrahman4u/keras-contrib

Covariate shift

• https://www.quora.com/What-is-Covariate-shift
• https://blog.bigml.com/2013/11/01/machine-learning-next/
• https://blog.bigml.com/2013/03/12/machine-learning-from-streaming-data-two-problems-two-solutions-two-concerns-and-two-lessons/

#PydataLondon2017

• https://pydata.org/london2017/schedule/presentation/12/
• https://pydata.org/london2017/schedule/presentation/20/
• https://pydata.org/london2017/schedule/presentation/34/
• https://pydata.org/london2017/schedule/presentation/17/
• https://pydata.org/london2017/schedule/presentation/47/
• https://pydata.org/london2017/schedule/presentation/16/
• https://pydata.org/london2017/schedule/presentation/52/
• https://pydata.org/london2017/schedule/presentation/22/
• https://pydata.org/london2017/schedule/presentation/30/
• https://pydata.org/london2017/schedule/presentation/23/
• https://pydata.org/london2017/schedule/presentation/69/

NLP course

• https://www.cs.bgu.ac.il/~elhadad/nlp17.html

Dataset

• CONLL2003: https://github.com/kuruonur1/char-tag
• https://files.pushshift.io/reddit/comments/

Tricks of DL

• https://engineering.purdue.edu/~qobi/papers/ad2016d.pdf
• practical DL: http://www.deeplearningbook.org/slides/11_practical.pdf
• tuning cnn: http://lamda.nju.edu.cn/weixs/project/CNNTricks/CNNTricks.html
• https://github.com/Conchylicultor/Deep-Learning-Tricks
• https://cs224d.stanford.edu/lectures/CS224d-Lecture6.pdf
• http://karpathy.github.io/neuralnets/
• http://www.deeplearningbook.org/slides/11_practical.pdf

Pointer network

• http://fastml.com/introduction-to-pointer-networks/
• keras: https://github.com/zygmuntz/pointer-networks-experiments
• https://arxiv.org/pdf/1511.06391v4.pdf
• https://www.slideshare.net/KeonKim/attention-mechanisms-with-tensorflow

Attention

• https://arxiv.org/abs/1707.00110

Log likelihood test

• tool http://ucrel.lancs.ac.uk/llwizard.html
• significance testing of word frequency in corpora: https://users.ics.aalto.fi/lijffijt/articles/lijffijt2015a.pdf
• TA and TM for social: https://de.dariah.eu/tatom/
• http://sappingattention.blogspot.com/2011/10/comparing-corpuses-by-word-use.html#comments
• http://sappingattention.blogspot.com/2011/11/dunning-amok.html
• https://tedunderwood.com/2011/11/09/identifying-the-terms-that-characterize-an-author-or-genre-why-dunnings-may-not-be-the-best-method/

---

MLtrainings.ru

• quora presentation: https://gh.mltrainings.ru/presentations/Skornyakov_KaggleQuora_2017.pdf
• hearthstone: https://gh.mltrainings.ru/presentations/Patekha_Hearthstone_2017.pdf

GCloud

• http://www.albertauyeung.com/post/setup-jupyter-nginx-supervisor/
• https://medium.com/google-cloud/running-jupyter-notebooks-on-gpu-on-google-cloud-d44f57d22dbd

Current conference

• http://sigir.org/sigir2017/
• icml: https://2017.icml.cc/
• emnlp: http://emnlp2017.net/

https://github.com/aymericdamien/TensorFlow-Examples

Timeline

07.12

• bayesian variable explanation: https://www.kdnuggets.com/2017/11/bayesian-networks-understanding-effects-variables.html
• end2end ML/DL https://aws.amazon.com/sagemaker/ (colab?)
• test of time https://www.youtube.com/watch?time_continue=2&v=Qi1Yry33TQE

06.12

• http://proceedings.mlr.press/v7/niculescu09/niculescu09.pdf
• https://www.nature.com/articles/d41586-017-07522-z
• http://www.wiseathena.com/pdf/wa_dl.pdf

05.12

• https://www.dataiku.com/learn/guide/tutorials/churn-prediction.html
• https://www.dataiku.com/solutions/use-cases/lifetime-value-optimisation/

04.12

• scikit optimize https://www.youtube.com/watch?v=DGJTEBt0d-s
• https://github.com/fmfn/BayesianOptimization/blob/master/examples/xgboost_example.py
• https://www.dataapplab.com/wp-content/uploads/2017/05/DAL-Kaggle-cometition.pdf

02.12

online marketing applications

• https://pydata.org/carolinas2016/schedule/presentation/23/
• https://github.com/maoting1223/pycon_sg_2016
• https://www.youtube.com/watch?v=gx6oHqpRgpY

01.12

• https://github.com/latuannetnam/kaggle

30.11

• https://hbr.org/2017/06/a-refresher-on-ab-testing
• Reuters Tracer: https://arxiv.org/pdf/1711.04068.pdf
• https://github.com/DmitryUlyanov/deep-image-prior
• https://research.googleblog.com/2017/11/interpreting-deep-neural-networks-with.html

29.11

• https://www.nytimes.com/2017/11/28/technology/artificial-intelligence-research-toronto.html
• https://rare-technologies.com/machine-learning-hardware-benchmarks/
• how xgboost handle nans: dmlc/xgboost#21
• https://github.com/ledmaster?tab=repositories
• automata extraction from RNN https://arxiv.org/pdf/1711.09576.pdf
• shap vis: https://github.com/slundberg/shap
• http://www.cs.jhu.edu/~ayuille/courses/Stat161-261-Spring14/Big%20data_%20are%20we%20making%20a%20big%20mistake_%20-%20FT.pdf
• http://cdn2.hubspot.net/hub/215445/file-1390429685-pdf/DI_ebook_-_How_to_Build_and_Lead_a_Winning_Data_Team-1.pdf?t=1435065619454

28.11

• https://www.aarki.com/blog/using-machine-learning-to-predict-campaign-performance
• https://www.forbes.com/sites/forbesagencycouncil/2017/11/15/how-machine-learning-can-maximize-the-success-of-marketing-campaigns/3/#4bbeb8df7846

27.11

• https://www.slideshare.net/DataRobot/featurizing-log-data-before-xgboost
• https://www.slideshare.net/DataRobot/make-sense-out-of-data-with-feature-engineering
• https://www.slideshare.net/KaiX/xavier-conort-datascience-sg-meetup-challenges-in-insurance-pricing
• https://www.slideshare.net/KaiX/forecasting-techniques-data-science-sg
• https://github.com/thiakx?tab=repositories
• https://www.kdnuggets.com/2017/11/ng-deep-learning-specialization-21-lessons.html?utm_content=buffera7008&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer
• https://www.slideshare.net/DataRobot/featurizing-log-data-before-xgboost
• https://github.com/gigamailer/simplenin/blob/master/Mastering%20Feature%20Engineering%20%2528Early%20Release%2529-O%2527Reilly%25282016%2529.pdf
• https://github.com/svegapons/kaggle_airbnb/blob/master/code_keras.py

24.11

• https://www.bloomberg.com/company/announcements/bloomberg-magic-machine-learning/
• https://www.investopedia.com/terms/a/alpha.asp
• http://web.nchu.edu.tw/~jodytsao/MarkegingG/IIR10-Sentiment%20Analysis.pdf
• https://flyyufelix.github.io/2017/11/17/direct-future-prediction.html
• https://medium.com/@jeffykao/more-than-a-million-pro-repeal-net-neutrality-comments-were-likely-faked-e9f0e3ed36a6

23.11

• http://blog.paralleldots.com/data-science/breakthrough-research-papers-and-models-for-sentiment-analysis/?lipi=urn%3Ali%3Apage%3Ad_flagship3_pulse_read%3BiC%2Fq1jhKSuCkAgj9YxVOuQ%3D%3D
• https://github.com/Far0n/xgbfi

22.11

• https://github.com/CleverTap/Analytics_ds_articles/tree/master/Data-Informed/Feature_Engineering
• https://towardsdatascience.com/diary-of-a-data-scientist-at-booking-com-924734c71417
• A/B testing at Booking https://arxiv.org/pdf/1710.08217.pdf
• https://booking.ai/named-entity-classification-d14d857cb0d5
• https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.stats.mstats.winsorize.html
• http://data-informed.com/how-to-improve-machine-learning-tricks-and-tips-for-feature-engineering/

21.11

• https://www.analyticsvidhya.com/blog/2017/06/which-algorithm-takes-the-crown-light-gbm-vs-xgboost/
• tune lightgbm: https://github.com/Microsoft/LightGBM/blob/master/docs/Parameters-Tuning.rst
• https://github.com/mdda/compressing-word-embeddings/tree/master/notebooks
• https://dashee87.github.io/deep%20learning/python/predicting-cryptocurrency-prices-with-deep-learning/
• https://github.com/dashee87/blogScripts/blob/master/Jupyter/2017-11-20-predicting-cryptocurrency-prices-with-deep-learning.ipynb

17.11

• https://medium.com/searchink-eng/keras-horovod-distributed-deep-learning-on-steroids-94666e16673d
• https://us3.campaign-archive.com/?u=6a29d4cc0471455d38260b3cc&id=ddf2eee959
• http://wangzhinan.com/2017/02/20/wsdm17-summary/#more

16.11

• deep ensembling: https://cambridgespark.com/content/tutorials/neural-networks-tuning-techniques/index.html
• https://www.technologyreview.com/s/609495/ai-can-be-made-legally-accountable-for-its-decisions/?utm_source=twitter.com&utm_medium=social&utm_content=2017-11-15&utm_campaign=Technology+Review
• https://beamandrew.github.io/deeplearning/2017/06/04/deep_learning_works.html
• https://github.com/taolei87/rcnn
• https://research.googleblog.com/2017/11/sling-natural-language-frame-semantic.html
• model intepretation: https://blog.kjamistan.com/towards-interpretable-reliable-models/
• https://github.com/cgnorthcutt/rankpruning
• https://github.com/PAIR-code/facets/blob/master/facets_overview/Overview_demo.ipynb
• https://github.com/google/sling

15.11

• https://github.com/catboost/catboost/blob/master/catboost/tutorials/quora_catboost_w2v.ipynb
• https://spacy.io/usage/v2

14.11

• https://github.com/PipelineAI/pipeline

13.11

• https://github.com/kaz-Anova/StackNet
• https://tech.yandex.com/catboost/doc/dg/concepts/python-reference_catboostclassifier_fit-docpage/
• https://machinelearning.apple.com/2017/10/01/hey-siri.html
• MLConf SF 2017: https://www.slideshare.net/JuneAndrews/counter-intuitive-machine-learning-for-the-industrial-internet-of-things-81862870/1
• https://www.slideshare.net/SessionsEvents
• https://towardsdatascience.com/7-takeaways-from-mlconf-sf-1b2703db5ecb

10.11

• what wrong with CNN: https://www.youtube.com/watch?v=rTawFwUvnLE
• https://medium.com/@culurciello/deep-neural-network-capsules-137be2877d44

09.11

• vizuka: https://github.com/0011001011/Vizuka
• https://www.youtube.com/watch?feature=youtu.be&v=klYBPl1ljTQ&list=PLGVZCDnMOq0rjkF7p_F4qtaVJQnjK1oKT&app=desktop

08.11

• pearson correlation: https://en.wikipedia.org/wiki/Pearson_correlation_coefficient
• jensen inequality: https://en.wikipedia.org/wiki/Jensen%27s_inequality
• ui2code: https://uizard.io/
• https://pypi.python.org/pypi/textstat/
• mse vs pearson correlation: http://www.bwgriffin.com/gsu/courses/edur8132/notes/Notes8c2_RegressionModelFit.pdf

3.11

• https://hackernoon.com/latest-deep-learning-ocr-with-keras-and-supervisely-in-15-minutes-34aecd630ed8

2.11

• https://github.com/XifengGuo/CapsNet-Keras/blob/master/CapsNet.py
• outlier detection: http://bugra.github.io/work/notes/2014-03-31/outlier-detection-in-time-series-signals-fft-median-filtering/
• actionable classification: https://arxiv.org/abs/1607.02501
• https://www.youtube.com/watch?v=NOUMgThZ5UE
• http://www.swisstext.org/#daeniken
• http://people.inf.ethz.ch/ganeao/emnlp17_deep_ed.pdf
• http://www.swisstext.org/docs/2017/Presentation/daeniken/swisstext_pius_von_daeniken.pdf
• http://www.swisstext.org/docs/2017/Presentation/pappas/swisstext17.pdf
• http://www.swisstext.org/docs/2017/Presentation/pappas/swisstext17.pdf

1.11

• two sample test, mean: https://www.isixsigma.com/tools-templates/hypothesis-testing/making-sense-two-sample-t-test/
• two sample test, ratio: https://github.com/maoting1223/pycon_sg_2016
• welchs test vs t student: http://daniellakens.blogspot.com/2015/01/always-use-welchs-t-test-instead-of.html

31.10

• structure data: https://github.com/random-forests/tensorflow-workshop/blob/master/examples/07_structured_data.ipynb
• https://www.pyimagesearch.com/2017/10/30/how-to-multi-gpu-training-with-keras-python-and-deep-learning/
• kaggle survey: LR first, tree second: https://www.kaggle.com/surveys/2017
• fe best practice: https://www.quora.com/What-are-some-best-practices-in-Feature-Engineering
• ppmi vs svd: https://github.com/piskvorky/word_embeddings/blob/master/run_embed.py
• class imbalance in cnn: https://arxiv.org/pdf/1710.05381.pdf
• rnnvis: https://arxiv.org/pdf/1710.10777.pdf
• task detection from email: https://medium.com/@rodrigo_23805/extracting-tasks-from-emails-first-challenges-86e7fbbf4672
• interactive cm: https://rare-technologies.com/interactive-confusion-matrix-python/

30.10

• radim newsletter http://us3.campaign-archive.com/?u=6a29d4cc0471455d38260b3cc&id=9f47229ab0
• prodLDA in keras: https://github.com/nzw0301/keras-examples/blob/master/prodLDA.ipynb
• prodLDA: https://openreview.net/pdf?id=BybtVK9lg
• bounter: https://github.com/RaRe-Technologies/bounter
• http://cikm2017.org/mainconschedule.html
• http://gael-varoquaux.info/stats_in_python_tutorial/
• http://matthewrocklin.com/blog/work/2017/10/16/streaming-dataframes-1?utm_campaign=Data%2BElixir&utm_medium=email&utm_source=Data_Elixir_154
• GA: http://blog.otoro.net/2017/10/29/visual-evolution-strategies/
• nlp talk: https://www.cs.umb.edu/~twang/file/cs188_TongWang.pdf
• http://yutori-datascience.hatenablog.com/entry/2017/10/29/205433

29.10

• linguistic structure is back, acl 2017: http://www.abigailsee.com/2017/08/30/four-deep-learning-trends-from-acl-2017-part-1.html

28.10

• https://www.bloomberg.com/graphics/2017-wall-street-robots/

27.10

• https://www.kaggle.com/knowledgegrappler/magic-embeddings-keras-a-toy-example

26.10

• Coursera kaggle: https://www.coursera.org/learn/competitive-data-science

25.10

• how to start ML/DL/NLP https://drive.google.com/file/d/0B2cCJQ2_aOwjUmFnRko2QjRGelE/view
• https://www.slideshare.net/lopusz/debugging-machinelearning
• https://github.com/meereeum/lda2vec-tf
• https://medium.com/@rchang/advice-for-new-and-junior-data-scientists-2ab02396cf5b
• https://github.com/YuriyGuts/kaggle-quora-question-pairs/blob/master/notebooks/classify-lightgbm-cv-pred.ipynb

24.10

• https://github.com/plaidml/plaidml
• https://www.youtube.com/watch?v=G4uDBe28ryQ
• https://github.com/ilkarman/DeepLearningFrameworks

23.10

• https://github.com/leondz/entity_recognition

20.10

• https://docs.google.com/presentation/d/1vFlR9QJ4v1XnRg0-sNhe0_1gZUjj1utDdAUHScjzOtI/edit#slide=id.g271203ffb6_2_8
• http://matrixmultiplication.xyz
• http://blog.yhat.com/posts/logistic-regression-python-rodeo.html

19.10

• https://jeremykun.com/2016/04/18/singular-value-decomposition-part-1-perspectives-on-linear-algebra/
• http://multithreaded.stitchfix.com/blog/2017/10/18/stop-using-word2vec/
• https://github.com/uber/horovod

18.10

• swish = x.sigmoid(x) https://arxiv.org/pdf/1710.05941.pdf
• DrQA: document retriever, document reader: https://github.com/facebookresearch/DrQA
• https://gist.github.com/GaelVaroquaux/ead9898bd3c973c40429

17.10

• outlier detection: https://storage.googleapis.com/supplemental_media/udacityu/3104648634/Hodge+Austin_OutlierDetection_AIRE381.pdf
• https://lilianweng.github.io/lil-log/2017/09/28/anatomize-deep-learning-with-information-theory.html
• opening the black box of DNN: https://arxiv.org/pdf/1703.00810.pdf
• information plane for DL: https://www.youtube.com/watch?v=bLqJHjXihK8
• information theory with C.Olah: http://colah.github.io/posts/2015-09-Visual-Information/

16.10

• https://developers.soundcloud.com/blog/soundclouds-data-science-process

15.10

• nlp curator: https://github.com/Kyubyong/nlp_tasks
• https://github.com/Kulbear/deep-learning-coursera

13.10

• Information theory of DL https://www.youtube.com/watch?v=RKvS958AqGY
• https://arxiv.org/pdf/1709.03856.pdf

12.10

• https://github.com/facebookresearch/StarSpace
• https://www.youtube.com/watch?v=aircAruvnKk&feature=youtu.be
• https://research.googleblog.com/2017/10/tensorflow-lattice-flexibility.html

11.10

• book stats learning of Hastie: https://web.stanford.edu/~hastie/CASI_files/PDF/casi.pdf?utm_content=bufferaea53&utm_medium=social&utm_source=linkedin.com&utm_campaign=buffer
• http://www.recognition.mccme.ru/pub/RecognitionLab.html/slbook.pdf
• https://www.ted.com/talks/jeremy_howard_the_wonderful_and_terrifying_implications_of_computers_that_can_learn
• tsne map: https://artsexperiments.withgoogle.com/tsnemap/#2072.02,145.27,5710.37,2039.00,138.00,5689.00
• http://people.cs.umass.edu/~brenocon/inlp2016/lectures/05,06-classif-scan.pdf
• capsules https://research.google.com/pubs/pub46351.html
• http://www.cs.toronto.edu/~fritz/absps/transauto6.pdf
• http://cseweb.ucsd.edu/~gary/cs200/s12/Hinton.pdf

10.10

• https://www.analyticsvidhya.com/blog/2017/06/word-embeddings-count-word2veec/
• https://github.com/esokolov/ml-course-hse/blob/master/2016-fall/lecture-notes/lecture11-dl.pdf

07.10

• https://arxiv.org/pdf/1710.00027.pdf

05.10

• emoji2 https://medium.com/huggingface/understanding-emotions-from-keras-to-pytorch-3ccb61d5a983
• attention layer: https://gist.github.com/thomwolf/e309e779a08c1ba899514d44355cd6df#file-attention_layer_keras-py

04.10

• hard sigmoid: https://stackoverflow.com/questions/35411194/how-is-hard-sigmoid-defined
• https://data.world/rickyhennessy/startup-names-and-descriptions/workspace/file?filename=startups.csv
• position attention bi-lstm: https://arxiv.org/pdf/1703.10089.pdf
• https://obilaniu6266h16.wordpress.com/2016/02/04/einstein-summation-in-numpy/
• https://arxiv.org/pdf/1512.04916.pdf
• oov: https://github.com/cheng6076/SNLI-attention/blob/master/oov_vec.py
• https://fasttext.cc/blog/2017/10/02/blog-post.html
• https://fasttext.cc/docs/en/language-identification.html
• https://teachablemachine.withgoogle.com/
• https://www.edvancer.in/machine-learning-vs-statistics/
• https://www.slideshare.net/nikhildandekar/maintaining-high-quality-user-generated-content-through-machine-learning

03.10

• https://statweb.stanford.edu/~candes/talks/Wald1.pdf
• http://aimotion.blogspot.com/2011/11/machine-learning-with-python-logistic.html
• https://arxiv.org/pdf/1705.08039.pdf
• https://medium.com/@shanif/our-data-science-workflow-b974f30a124d
• http://u.cs.biu.ac.il/~yogo/DepLing2017invited.pdf

02.10

• https://github.com/DataScienceUB/DeepLearningfromScratch
• https://medium.com/applied-data-science/new-r-package-the-xgboost-explainer-51dd7d1aa211
• https://medium.com/@shanif/our-data-science-workflow-b974f30a124d
• https://www.slideshare.net/GaelVaroquaux/computational-practices-for-reproducible-science

30.09

• https://developers.google.com/machine-learning/glossary/
• https://www.slideshare.net/GaelVaroquaux/computational-practices-for-reproducible-science
• https://github.com/SSDS-Croatia/SSDS-2017
• https://sites.google.com/site/ssdatascience2017/lecture-notes

29.09

• feature selection multiple hypothesis testing: http://kelvinguu.com/posts/feature-selection-and-multiple-hypothesis-testing/
• how to do feature selection correctly: http://kelvinguu.com/posts/why-naive-cross-validation-fails-at-feature-selection/
• https://habrahabr.ru/post/326122/
• http://soloro.ru
• http://kelvinguu.com/
• http://jakob.uszkoreit.net/
• coarse to fine QA for long document: https://arxiv.org/pdf/1611.01839.pdf
• generating sentences by editing prototypes: https://arxiv.org/pdf/1709.08878.pdf

28.09

• http://ruder.io/optimizing-gradient-descent/
• https://github.com/kuza55/keras-extras/blob/master/layers/DiffForest.py
• https://arxiv.org/pdf/1702.08835.pdf
• https://docs.google.com/presentation/d/1Ze7BAiWbMPyF0ax36D-aK00VfaGMGvvgD_XuANQW1gU/edit#slide=id.p
• https://uima.apache.org/

27.09

• https://arxiv.org/pdf/1608.01238.pdf
• https://web.stanford.edu/~jurafsky/slp3/16.pdf
• http://www.aclweb.org/anthology/N12-2009
• https://web.stanford.edu/~jurafsky/slp3/ed3book.pdf

25.09

• brown cluster: https://arxiv.org/pdf/1608.01238.pdf
• word sense: http://www.cs.columbia.edu/~mcollins/courses/6998-2011/lectures/yarowsky.pdf
• http://www.derczynski.com/sheffield/papers/brown_impact.pdf
• http://people.cs.georgetown.edu/cosc572/f16/21b_dist_slides.pdf
• https://paulx-cn.github.io/blog/5th_Blog/

22.09

• http://blog.kaggle.com/2017/09/21/instacart-market-basket-analysis-winners-interview-2nd-place-kazuki-onodera/

21.09

• rossmann nnet https://arxiv.org/pdf/1604.06737.pdf
• http://blog.kaggle.com/2016/01/22/rossmann-store-sales-winners-interview-3rd-place-cheng-gui/
• https://kaggle2.blob.core.windows.net/forum-message-attachments/102102/3454/Rossmann_nr1_doc.pdf

19.09

• memory augmented nnet for nlp: https://drive.google.com/file/d/0B9dqzboiV5u-UmxJQlJqcUl6anM/view
• kaggle quora blog: https://indatalabs.com/blog/data-science/how-to-win-kaggle-competition

18.09

• http://u.cs.biu.ac.il/~yogo/DepLing2017invited.pdf
• http://newsletter.ruder.io/issues/nlp-news-review-of-emnlp-2017-analyzing-bias-google-brain-ama-dragnn-and-allennlp-72584

17.09

• http://xrds.acm.org/blog/2017/07/power-wordnet-use-python/
• https://simons.berkeley.edu/sites/default/files/docs/5950/2017.02.01-21.15.12-simons-nlp-tutorial.pdf
• talking to machine: http://cs.stanford.edu/~pliang/papers/talking-xrds2014.pdf
• zero learning talk: https://www.youtube.com/watch?v=6O5sttckalE

16.09

• https://github.com/philipperemy/tensorflow-class-activation-mapping

15.09

• http://www.cs.tut.fi/kurssit/SGN-2556/slides/Lecture6.pdf

14.09

• https://cloud.google.com/blog/big-data/2017/01/learn-tensorflow-and-deep-learning-without-a-phd

13.09

• strong algos: GBT, RF, SVM for classification: https://arxiv.org/pdf/1708.05070.pdf
• https://medium.com/slalom-engineering/detecting-malicious-requests-with-keras-tensorflow-5d5db06b4f28
• https://github.com/tensorflow/workshops
• https://github.com/chuckyee/cardiac-segmentation
• real time CNN: https://github.com/lampts/face_classification/blob/master/technical_report.pdf

12.09

• https://en.wikipedia.org/wiki/White_Noise_(novel)
• hitchhike guide to the galaxy:
• https://www.cs.bgu.ac.il/~yoavg/uni/bloglike/baboons.html
• http://u.cs.biu.ac.il/~yogo/courses/sem2017/

11.09

• word embedding Komninos https://www.cs.york.ac.uk/nlp/extvec/
• https://ku.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=0954a17c-2702-4d8e-9412-12ae958a2790
• score distribution is better: https://arxiv.org/abs/1707.09861
• make a stable architecture: https://arxiv.org/abs/1707.06799, pretrained embedding, last layer of lstm is crucial.
• https://github.com/lanwuwei/paraphrase-dataset
• why non convex: https://github.com/lanwuwei/paraphrase-dataset
• https://www.reddit.com/r/dataisbeautiful/comments/6ykfvl/average_word_length_for_nytimes_crossword_answers/

10.09

• dilated convnet https://medium.com/@TalPerry/convolutional-methods-for-text-d5260fd5675f
• quora view: https://www.quora.com/challenges#views

09.09

• https://ydkahin.github.io/blog/views-prediction---a-quora-challenge---part-iii-eda-feature-engineering-and-more/?utm_content=buffera82c7&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer
• https://github.com/Unbabel/
• https://andre-martins.github.io/docs/emnlp2017_final.pdf
• http://allennlp.org/tutorials/configuration

08.09

• https://www.eff.org/ai/metrics
• http://courses.wcupa.edu/rbove/Berenson/10th%20ed%20CD-ROM%20topics/section12_5.pdf
• percy liang: http://shrdlurn.sidaw.xyz/acl16/
• https://www.youtube.com/watch?v=mhHfnhh-pB4
• https://manning-content.s3.amazonaws.com/download/d/bcdc8c6-3f2e-4a2d-974b-487fc1da7cdf/Chollet_DLwPython_MEAP_V05_ch1.pdf
• http://ofir.io/Neural-Language-Modeling-From-Scratch/
• https://www.thoughtco.com/normal-approximation-to-the-binomial-distribution-3126589

07.09

• https://www.thoughtco.com/normal-approximation-to-the-binomial-distribution-3126589
• http://www.stat.purdue.edu/~xuanyaoh/stat350/xyJan23Lec4.pdf
• https://github.com/tensorflow/serving/blob/master/tensorflow_serving/example/mnist_client.py
• https://medium.com/towards-data-science/how-to-deploy-machine-learning-models-with-tensorflow-part-2-containerize-it-db0ad7ca35a7
• https://medium.com/towards-data-science/how-to-deploy-machine-learning-models-with-tensorflow-part-3-into-the-cloud-7115ff774bb6
• https://github.com/Vetal1977/tf_serving_example
• https://github.com/udacity/deep-learning/blob/master/semi-supervised/semi-supervised_learning_2_solution.ipynb

06.09

• https://medium.com/towards-data-science/how-to-deploy-machine-learning-models-with-tensorflow-part-2-containerize-it-db0ad7ca35a7
• https://medium.com/zendesk-engineering/how-zendesk-serves-tensorflow-models-in-production-751ee22f0f4b
• https://github.com/lampts/deep-learning-with-python-notebooks/blob/master/3.5-classifying-movie-reviews.ipynb

05.09

• ds interview: http://www.thedsinterview.com/
• 4 trends: structure is back, re embedding, blackbox transparency, attention: http://www.abigailsee.com/2017/08/30/four-deep-learning-trends-from-acl-2017-part-2.html
• https://github.com/UKPLab/emnlp2017-relation-extraction
• intepret rnn: https://github.com/philipperemy/tensorflow-isan-rnn

04.09

• http://theorangeduck.com/page/neural-network-not-working
• https://dzone.com/articles/natural-language-processing-adit-deshpande-cs-unde
• https://github.com/ddtm/dl-course

03.09

• http://multithreaded.stitchfix.com/blog/2017/08/31/warehouse-layouts/
• https://machinelearningmastery.com/diagnose-overfitting-underfitting-lstm-models/

02.09

• https://github.com/AlexandreRobicquet?tab=repositories
• https://pillbox.nlm.nih.gov/developer.html#images

01.09

• http://artemis-ml.readthedocs.io/en/latest/plotting.html
• https://github.com/krystianity/keras-serving
• https://github.com/Lausbert/Exermote/tree/master/ExermotePreprocessingAndTraining

31.08

• http://liufuyang.github.io/2017/04/02/just-another-tensorflow-beginner-guide-4.html
• https://github.com/Lausbert/Exermote/blob/master/ExermotePreprocessingAndTraining/trainer/exermote.py
• http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.LeaveOneOut.html

30.08

• effective tf: https://github.com/vahidk/EffectiveTensorflow
• knn and bilstm https://arxiv.org/pdf/1708.07863.pdf
• https://nlp.stanford.edu/pubs/jia2017adversarial.pdf
• https://github.com/dformoso/machine-learning-mindmap

29.08

• https://medium.com/@burgalon/deploying-your-keras-model-using-keras-js-2e5a29589ad8

28.08

• https://nlp.stanford.edu/courses/cs224n/2015/reports/29.pdf
• https://becominghuman.ai/cheat-sheets-for-ai-neural-networks-machine-learning-deep-learning-big-data-678c51b4b463?_lrsc=ce853194-65af-4e5e-a424-7d21025fd0c9
• https://blog.fineighbor.com/tensorflow-dealing-with-imbalanced-data-eb0108b10701
• https://arxiv.org/pdf/1707.05127.pdf

26.08

• https://github.com/zalandoresearch/fashion-mnist/blob/master/README.md
• https://github.com/DrMichaelWang/Kaggle_Cancer_Project/blob/master/Kaggle%20cancer%20-%20text%20key%20word%20frequency%20count_xgboost.ipynb

25.08

• http://krisztianbalog.com/
• https://medium.com/@erogol/designing-a-deep-learning-project-9b3698aef127
• https://github.com/idiap/importance-sampling
• http://krisztianbalog.com/files/talks/russir2016-el.pdf
• https://github.com/kbalog/russir2016-el

24.08

• http://blog.rtwilson.com/how-to-rescue-lost-code-from-a-jupyteripython-notebook/
• http://maxberggren.se/2017/06/18/deep-learning-vs-xgboost/
• http://beamandrew.github.io/deeplearning/2017/06/04/deep_learning_works.html

22.08

• https://gist.github.com/menshikh-iv/0c691219314da35f48f10826b6d34d97
• https://github.com/minimaxir/reactionrnn
• http://www.kdnuggets.com/2017/08/oreilly-nyc-ai-conference-highlights.html
• https://speakerdeck.com/tmylk/pycon-russia-2017-tiematichieskoie-modielirovaniie-dlia-liudiei
• http://newsletter.ruder.io/issues/nlp-news-data-selection-ml-nlp-in-esports-vqa-bias-lyric-annotations-68803
• https://github.com/fchollet/keras/releases/tag/2.0.7

21.08

• https://github.com/rasbt/python-machine-learning-book-2nd-edition
• https://github.com/sjvasquez/instacart-basket-prediction

18.08

• http://evexdb.org/pmresources/vec-space-models/

17.08

• https://medium.com/the-mission/a-genius-explains-how-to-be-creative-claude-shannons-long-lost-1952-speech-fbbcb2ebe07f

16.08

• http://mltrainings.ru/
• asap https://github.com/ddofer/asap/wiki/Getting-Started:-A-Basic-Tutorial
• https://arxiv.org/pdf/1701.08318.pdf
• genome modeling: https://cs224d.stanford.edu/reports/jessesz.pdf
• https://www.reddit.com/r/MachineLearning/comments/6tu9gu/what_is_the_process_of_deploying_machine_learning/?st=j6ee7uoq&sh=12c17107
• https://github.com/chrisranderson/beholder
• https://github.com/rasbt/deep-learning-book

15.08

• http://bayes.wustl.edu/etj/prob/book.pdf

14.08

• https://github.com/experiencor/deep-viz-keras
• https://github.com/facebookresearch/SentEval
• http://machinelearningmastery.com/reproducible-results-neural-networks-keras/
• https://github.com/rasbt/deep-learning-book/blob/master/code/model_zoo/file-queues.ipynb
• https://github.com/nlml/np-to-tf-embeddings-visualiser/blob/master/save_embeddings.py

13.08

• https://github.com/vahidk/EffectiveTensorflow

11.08

• http://chri.stophr.be/
• https://github.com/nadbordrozd/text-top-model/tree/master/ttm/keras_models
• https://tryolabs.com/blog/2017/08/10/finding-the-right-representation-for-your-nlp-data/
• https://www.mira.law/blogposts/2017/5/12/semantic-averaging-of-documents-using-word2vec-representations

10.08

• https://github.com/cpury/lstm-math

09.08

• dl course: https://www.coursera.org/specializations/deep-learning

08.08

• roc auc: http://www.navan.name/roc/
• https://worksheets.codalab.org/worksheets/0x50757a37779b485f89012e4ba03b6f4f/
• https://nlp.stanford.edu/pubs/jia2016recombination.pdf

07.08

• best paper ICML: https://github.com/mlresearch/v70
• https://explosion.ai/blog/prodigy-annotation-tool-active-learning
• https://github.com/brannondorsey/keras_weight_animator
• https://github.com/keveman/tensorflow-tutorial/blob/master/PTB%20Word%20Language%20Modeling.ipynb

06.08

• https://github.com/brannondorsey/keras_weight_animator
• https://github.com/pavitrakumar78/Anime-Face-GAN-Keras
• https://code.facebook.com/posts/289921871474277/transitioning-entirely-to-neural-machine-translation/
• https://prodi.gy/demo
• https://prodi.gy/docs/

04.08

• emoji transfer learning: https://arxiv.org/pdf/1708.00524.pdf
• http://deepmoji.mit.edu/
• importance sampling https://arxiv.org/pdf/1706.00043.pdf
• larochelle https://drive.google.com/file/d/0ByUKRdiCDK7-LXZkM3hVSzFGTkE/view
• bengio https://drive.google.com/file/d/0ByUKRdiCDK7-UXB1R1ZpX082MEk/view

01.08

• pca with jake http://nbviewer.jupyter.org/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.09-Principal-Component-Analysis.ipynb
• https://openreview.net/pdf?id=HyaF53XYx

31.07

• http://casa.disi.unitn.it/~moschitt/Teaching-slides/slides-AINLP-2016/NER&POS-AINLP.pdf
• noise in feature space: https://openreview.net/pdf?id=HyaF53XYx
• data augmentation using thesaurus: https://arxiv.org/pdf/1509.01626.pdf
• https://theneuralperspective.com/
• http://casa.disi.unitn.it/~moschitt/since2013/2015_SIGIR_Severyn_TwitterSentimentAnalysis.pdf
• https://einstein.ai/research/state-of-the-art-deep-learning-model-for-question-answering
• https://sigmoidal.io/boosting-your-solutions-with-nlp/
• http://www.fast.ai/2017/07/28/deep-learning-part-two-launch/
• https://huyenchip.com/2017/07/28/confession.html
• https://blog.slavv.com/37-reasons-why-your-neural-network-is-not-working-4020854bd607

25.07

• how to ensemble https://mlwave.com/kaggle-ensembling-guide/
• https://www.slideshare.net/TedXiao/winning-kaggle-101-dmitry-larkos-experiences
• http://togelius.blogspot.se/2017/07/some-advice-for-journalists-writing.html
• https://sadanand-singh.github.io/posts/treebasedmodels/
• regression with keras: https://www.datacamp.com/community/tutorials/deep-learning-python

24.05

• data readiness: https://arxiv.org/pdf/1705.02245.pdf
• trophy data scientist: https://peadarcoyle.wordpress.com/2017/07/23/avoiding-being-a-trophy-data-scientist/
• best paper cvpr 17: https://arxiv.org/pdf/1608.06993.pdf, https://github.com/liuzhuang13/DenseNet
• https://github.com/titu1994/DenseNet
• https://github.com/UKPLab/emnlp2017-bilstm-cnn-crf

23.07

• https://github.com/greydanus/excitation_bp

22.07

• https://medium.com/huggingface/state-of-the-art-neural-coreference-resolution-for-chatbots-3302365dcf30
• https://github.com/bloomberg/scatteract
• http://gree2.github.io/ocr/2017/03/08/tesseract-ocr-parser-within-tika

21.07

• https://www.youtube.com/watch?v=5sQ8-Er8tXM
• https://github.com/HouJP/kaggle-quora-question-pairs
• http://www.andreykurenkov.com/writing/a-brief-history-of-neural-nets-and-deep-learning/

20.07

• https://github.com/hollance/YOLO-CoreML-MPSNNGraph

19.07

• https://pjreddie.com/darknet/yolo/
• ridge lr: http://www.utstat.toronto.edu/~guerzhoy/303/lec/lec8/ridge.pdf
• https://github.com/catboost/catboost/tree/master/catboost/tutorials

18.07

• https://kiko01b.wordpress.com/2011/07/16/replace-a-word-containing-a-slash-with-sed/
• https://stackoverflow.com/questions/11392478/how-to-replace-a-string-in-multiple-files-in-linux-command-line
• https://blog.keras.io/the-limitations-of-deep-learning.html
• https://github.com/pair-code/facets

17.07

• https://medium.com/@anandr42/the-data-science-delusion-7759f4eaac8e
• https://gist.github.com/menshikh-iv/0c691219314da35f48f10826b6d34d97
• http://www.fast.ai/2016/12/08/org-structure/
• https://github.com/sarchak/MachineLearningNotebooks
• nn for ir: https://arxiv.org/pdf/1707.04242.pdf
• https://github.com/LeiG/Applied-Predictive-Modeling-with-Python

15.07

• http://www.vjsonline.org/scientist-portrait/1500039392
• https://github.com/jeongyoonlee/data-science-process-management

14.07

• large csv: http://pythondata.com/working-large-csv-files-python/
• https://arimo.com/data-science/2016/bayesian-optimization-hyperparameter-tuning/
• bo https://github.com/phvu/misc/blob/master/sf_crimes/crimes_job_nn.py
• foolbox https://arxiv.org/abs/1707.04131
• http://www.aifounded.com/aifounded/recent-evolution-of-the-qa-datasets-and-going-forward/
• https://gist.github.com/thomasjungblut/b58d70d260abf0eff1a8c447f3d07389#file-xgb_bayes_opt_cv-py
• http://www.bosatsu.net/talks/sletten-datascience.pdf
• https://github.com/dipanjanS/text-analytics-with-python/blob/master/Chapter-6/document_similarity.py

13.07

• http://static.squarespace.com/static/51156277e4b0b8b2ffe11c00/t/53ad86e5e4b0b52e4e71cfab/1403881189332/Applied_Predictive_Modeling_in_R.pdf
• https://github.com/minimaxir/predict-reddit-submission-success
• https://www.google.com/finance/company_news?q=NASDAQ%3AFB&ei=ZA5nWaCMMImFsAG8p4ewCw

12.07

• https://github.com/organisciak/Text-Mining-Course
• http://news.efinancialcareers.com/uk-en/285249/machine-learning-and-big-data-j-p-morgan?utm_content=buffer29288&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer
• https://twitter.com/search?q=%23machinelearningflashcards&src=tyah

10.07

• https://github.com/Wrosinski/berlin-ml-article
• https://github.com/saulpw/visidata/blob/stable/docs/tours.rst
• social emnlp: https://twitterinadvertising.files.wordpress.com/2017/02/tweeted-about-742-times.pdf
• good pointers on nn: https://drive.google.com/file/d/0ByUKRdiCDK7-UXB1R1ZpX082MEk/view
• https://github.com/0xnurl/keras_character_based_ner
• https://www.aclweb.org/mirror/emnlp2016/proceedings/2016-emnlp-handbook.pdf

06.07

• https://nlp.stanford.edu/software/crf-faq.shtml
• Redcatlab: http://www.redcatlabs.com/2015-11-24_IES-2015_NER-from-Experts/
• embedding compression http://sei.pku.edu.cn/~moull12/paper/cikm16.pdf
• https://github.com/facebookresearch/InferSent

Maxout:

• https://github.com/philipperemy/tensorflow-maxout/blob/master/maxout.py
• https://arxiv.org/pdf/1302.4389.pdf

05.07

• working with text for social: https://de.dariah.eu/tatom/
• clickbait: https://github.com/saurabhmathur96/clickbait-detector
• http://blog.echen.me/2012/01/03/introduction-to-conditional-random-fields/
• http://nbviewer.jupyter.org/github/tpeng/python-crfsuite/blob/master/examples/CoNLL%202002.ipynb
• CRF: https://arjoonn.blogspot.com/2016/01/prerequisites-for-conditional-random.html
• NYT 1M: https://drive.google.com/file/d/0B0CbnDgKi0PyM1FEQXJRTlZtSTg/view
• https://github.com/davidsbatista/NER-English-Gigaword-LDC
• https://github.com/andreasvlachos/ALTA_ML_for_NLP

04.07

• https://www.slideshare.net/RasmusRothe/3-learnings-from-applying-deep-learning-to-real-world-problems
• pytorch vs tf: https://medium.com/@dubovikov.kirill/pytorch-vs-tensorflow-spotting-the-difference-25c75777377b
• https://github.com/Franck-Dernoncourt/NeuroNER/blob/master/trained_models/performances.md
• http://machinelearningmastery.com/adam-optimization-algorithm-for-deep-learning/
• https://offbit.github.io/how-to-read/

03.07

• https://mathematical-coffees.github.io/mc07-ml/
• ran: http://www.kentonl.com/pub/llz.2017.pdf
• https://www.microsoft.com/en-us/research/wp-content/uploads/2017/06/fntir-neuralir-mitra.pdf
• https://github.com/bdhingra/ga-reader/blob/master/model/GAReader.py
• https://github.com/allenai/deep_qa/tree/master/deep_qa/layers
• Gate for QA: https://arxiv.org/pdf/1606.01549.pdf
• TWINE https://www.aclweb.org/anthology/E/E17/E17-3007.pdf
• 30 nlp interview questions: https://www.analyticsvidhya.com/blog/2017/07/30-questions-test-data-scientist-natural-language-processing-solution-skilltest-nlp/
• mlss: http://nuit-blanche.blogspot.com/2017/06/slides-machine-learning-summer-school.html
• network analysis: http://i.stanford.edu/~jure/pub/talks2/leskovec-networks-01-nodes.pdf
• dl: http://mlss.tuebingen.mpg.de/2017/speaker_slides/Ruslan1.pdf, http://mlss.tuebingen.mpg.de/2017/speaker_slides/Ruslan2.pdf
• https://offbit.github.io/how-to-read/

02.07

• http://ianozsvald.com/2017/07/01/kaggles-mercedes-benz-greener-manufacturing/
• https://github.com/atveit/GANforiPhoneWithCoreML/blob/master/GAN.ipynb
• https://www.raywenderlich.com/164213/coreml-and-vision-machine-learning-in-ios-11-tutorial
• http://www.cs.nyu.edu/shasha/papers/StatisticsIsEasyExcerpt.html
• http://www.physics.csbsju.edu/stats/

30.06

• http://yerevann.com/a-guide-to-deep-learning/
• https://github.com/stitchfix/seetd
• https://github.com/minimaxir/facebook-page-post-scraper
• https://github.com/rykov8/ssd_keras
• https://github.com/yhenon/keras-frcnn
• https://github.com/niderhoff/nlp-datasets
• http://yerevann.github.io/2016/09/21/presentation-sentence-representations-and-question-answering/

29.06

• scorecard application: https://www.linkedin.com/pulse/credit-risk-scorecard-monitoring-tracking-shailendra
• http://cds.nyu.edu/wp-content/uploads/2014/04/bertini_datascience_showcase_May12_2014.pdf
• annotation tool: https://github.com/RicardoUsbeck/QRTool
• ned dataset: https://datahub.io/dataset/reuters-128-nif-ner-corpus

28.06

• wsd: https://web.stanford.edu/class/cs224n/reports/2762042.pdf
• speech and lang processing: http://www.cs.colorado.edu/~martin/slp.html
• nlp course: http://naviglinlp.blogspot.com/2017/
• ted dunning: http://aclweb.org/anthology/J93-1003
• http://tdunning.blogspot.com/2008/03/surprise-and-coincidence.html
• ll calculation: http://ucrel.lancs.ac.uk/llwizard.html
• http://www.prooffreader.com/2014/12/most-decade-specific-words-in-billboard.html
• https://github.com/Prooffreader/data-science-blogs
• http://www.prooffreader.com/2015/05/most-characteristic-words-in-pro-and.html
• https://github.com/zafarali?tab=repositories

27.06

• http://nikolenko.livejournal.com/275253.html
• CRF survey: http://nlpx.net/archives/464
• https://github.com/LopezGG/NN_NER_tensorFlow
• https://medium.com/hockey-stick/tl-dr-bayesian-a-b-testing-with-python-c495d375db4d
• https://alexanderdyakonov.files.wordpress.com/2017/06/book_boosting_pdf.pdf
• https://github.com/backstopmedia/tensorflowbook
• csi with tf: http://web.stanford.edu/class/cs20si/syllabus.html
• rnn in excel https://docs.google.com/spreadsheets/d/18bkheoJbmMUqdRFrviUy_TiooSjvvpDqiti7hm2EASY/edit#gid=0
• why elu not (relu) http://www.picalike.com/blog/2015/11/28/relu-was-yesterday-tomorrow-comes-elu/
• https://medium.com/@timanglade/how-hbos-silicon-valley-built-not-hotdog-with-mobile-tensorflow-keras-react-native-ef03260747f3
• https://gist.github.com/J-DM

26.06

• is it significant? http://www.ox.ac.uk/media/global/wwwoxacuk/localsites/uasconference/presentations/P8_Is_it_statistically_significant.pdf
• PSI: http://ucanalytics.com/blogs/population-stability-index-psi-banking-case-study/
• loan credit: http://ucanalytics.com/blogs/data-visualization-case-study-banking/
• FE: https://courses.cit.cornell.edu/cs5304/Lectures/lec5_FeatureEngineering.pdf
• https://github.com/maciejkula/recommender_datasets
• EL: https://github.com/namkhanhtran/EntityLinkingRetrieval-ELR
• https://github.com/raghakot/keras-vis
• https://gh.mltrainings.ru/presentations/Semenov_TinkoffChallenge_2017.pdf
• http://ucanalytics.com/blogs/information-value-and-weight-of-evidencebanking-case/

24.06

• https://github.com/klarsen1/Information

23.06

• http://multithreaded.stitchfix.com/blog/2015/08/13/weight-of-evidence/
• WOE: https://github.com/patrick201/information_value
• https://github.com/akashgit/autoencoding_vi_for_topic_models
• https://github.com/carpedm20/variational-text-tensorflow
• AVITM https://openreview.net/pdf?id=BybtVK9lg
• https://www.hackerearth.com/practice/machine-learning/advanced-techniques/winning-tips-machine-learning-competitions-kazanova-current-kaggle-3/tutorial/
• gensim 2.2.3 https://github.com/RaRe-Technologies/gensim/releases/tag/2.2.0
• tkm quora solution: https://www.slideshare.net/tkm2261/quora-76995457
• http://yutori-datascience.hatenablog.com/

22.06

• https://github.com/lampts/kaggle-quora-solution-8th
• https://github.com/Far0n/xgbfi
• http://microposts2016.seas.upenn.edu/challenge.html
• https://github.com/wikilinks/nel/blob/master/notebooks/train.ipynb
• http://www.semantic-web-journal.net/system/files/swj1562.pdf
• https://github.com/jeniyat/TweeTime

21.06

• sentiment corpus: https://www.w3.org/community/sentiment/wiki/Datasets
• paper2code: https://github.com/daviddao/awesome-very-deep-learning
• https://handong1587.github.io/deep_learning/2015/10/09/rnn-and-lstm.html
• task benchmark https://www.eff.org/ai/metrics
• http://willwolf.io/2017/06/15/random-effects-neural-networks/
• http://colah.github.io/posts/2015-08-Backprop/
• http://sdsawtelle.github.io/blog/output/getting-started-with-tensorflow-in-jupyter.html
• https://arxiv.org/pdf/1611.05418.pdf

19.06

• all you need is attention: https://github.com/Kyubyong/transformer
• http://damiano.github.io/learning-similarity-functions-ORM/
• https://github.com/abhishekkrthakur/clickbaits_revisited
• entity filtering and topic detection: thesis-DamianoSpina.pdf
• https://alexanderdyakonov.files.wordpress.com/2017/06/book_boosting_pdf.pdf
• https://github.com/ejmeij/entity-linking-and-retrieval-tutorial

14.06

• automating FE, OneBM: https://arxiv.org/pdf/1706.00327.pdf
• imbalance sklearn: https://glemaitre.github.io/talks/2017_PyParis/#1
• feature selection: http://www.kdnuggets.com/2017/06/practical-importance-feature-selection.html
• https://groups.google.com/a/tensorflow.org/forum/#!msg/discuss/Dhy9MseSXQI/naoy_EElBAAJ
• https://github.com/curiousily
• EL and ER: https://www.dropbox.com/sh/h7fr4yfrih6tisr/Q9BU8Qshcq?lst=

13.06

• https://github.com/ageron/handson-ml
• http://ft-interactive.github.io/visual-vocabulary/
• https://phvu.net/2016/05/13/count-featurizer/

12.06

• https://www.slideshare.net/HJvanVeen/kaggle-presentation
• https://medium.com/udacity/launching-astra-fab2b76b6420
• https://medium.com/@curiousily/tensorflow-for-hackers-part-ii-building-simple-neural-network-2d6779d2f91b
• http://alexanderdyakonov.narod.ru/lpot4emu.pdf
• https://github.com/turboNinja2/Homesite/blob/master/SubmissionsKeras.py

09.06

• https://medium.com/@yoav.goldberg/an-adversarial-review-of-adversarial-generation-of-natural-language-409ac3378bd7
• https://www.slideshare.net/HJvanVeen/feature-engineering-72376750

07.06

• https://medium.com/@curiousily/tensorflow-for-hackers-part-ii-building-simple-neural-network-2d6779d2f91b
• rnn in excel: https://docs.google.com/spreadsheets/d/18bkheoJbmMUqdRFrviUy_TiooSjvvpDqiti7hm2EASY/edit#gid=316082502
• http://nlp.cs.rpi.edu/paper/sigmod2016.pdf
• http://distill.pub/2016/augmented-rnns/
• http://xren7.web.engr.illinois.edu/KDD15-ClusType_v3.pdf
• gp: https://github.com/phvu/misc/blob/master/bayesopt/gaussian_process.py

05.06

• https://github.com/Franck-Dernoncourt/NeuroNER

02.06

• https://github.com/kailashahirwar/cheatsheets-ai/blob/master/All%20Cheat%20Sheets.pdf
• https://docs.microsoft.com/en-us/cognitive-toolkit/Using-CNTK-with-Keras

01.06

• https://github.com/georgeiswang/Query_Classfication_LSTM
• https://www.oreilly.com/ideas/language-understanding-remains-one-of-ais-grand-challenges
• AI and NLP: https://www.xenonstack.com/blog/overview-of-artificial-intelligence-and-role-of-natural-language-processing-in-big-data
• http://ndres.me/kaggle-past-solutions/
• https://github.com/UKPLab/semeval2017-scienceie
• http://www.nada.kth.se/~ann/exjobb/jan_vandekerkhof.pdf
• https://blog.booking.com/multivariant-tests-for-performance.html
• https://www.ambiverse.com/make-your-news-smarter/
• learn to search: https://hunch.net/~l2s/merged.pdf

31.05

• https://dennisforbes.ca/#a302
• http://www.namedevelopment.com/blog/default.html
• http://www.telegraph.co.uk/finance/personalfinance/comment/4478124/The-name-game.html
• http://new.opencalais.com/wp-content/uploads/2016/01/Thomson-Reuters-Intelligent-Tagging-On-Premise-API-User-Guide.pdf
• http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture10.pdf

30.05

• http://tkipf.github.io/graph-convolutional-networks/
• http://deeploria.gforge.inria.fr/thomasTalk.pdf
• graph cnn https://github.com/tkipf/gcn
• deeploria: http://deeploria.gforge.inria.fr/
• dedupe: https://github.com/dedupeio/dedupe
• http://sebastianruder.com/multi-task/index.html
• https://arxiv.org/pdf/1705.09585.pdf
• https://clgiles.ist.psu.edu/pubs/jcdl2015-name-disambiguation.pdf

29.05

• why PReLU, maxout: http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture6.pdf

26.05

• https://medium.springboard.com/interesting-talks-from-pydata-london-2017-d17b06c1ed5e
• https://github.com/DistrictDataLabs/yellowbrick
• https://github.com/lucjb/pydata2017/blob/master/Multicolinearity.py
• https://github.com/cavaunpeu/dotify/blob/master/notebooks/neural_implicit_mf.ipynb

25.05

• https://www.zanaducloud.com/CC6612B2-B42A-4765-A0C8-4FDB3CEF50E2
• http://willwolf.io/2017/05/18/minimizing_the_negative_log_likelihood_in_english/
• https://github.com/cavaunpeu/dotify/blob/master/notebooks/neural_implicit_mf.ipynb

21.05

• data interview: https://github.com/talolard/Interview
• https://medium.com/@nikasa1889/a-guide-to-receptive-field-arithmetic-for-convolutional-neural-networks-e0f514068807
• https://medium.com/@TalPerry/convolutional-methods-for-text-d5260fd5675f

20.05

• https://github.com/nelson-liu/paraphrase-id-tensorflow

19.05

• https://github.com/Microsoft/LightGBM/wiki/Installation-Guide
• https://github.com/ArdalanM/pyLightGBM

18.05

• https://github.com/Franck-Dernoncourt/NeuroNER
• https://128.84.21.199/pdf/1705.06273.pdf

17.05

• http://www.jaist.ac.jp/~bao/DS2017/BigData-I-Dinh-v4-4perPage.pdf

16.05

• https://www.youtube.com/watch?v=HS7mObQttxU
• https://en.wikipedia.org/wiki/BLEU
• http://www.mathsisfun.com/data/quincunx.html

15.05

• https://github.com/hengluchang/Quora-Paraphrase-Question-Identification
• online w2v: https://markroxor.github.io/gensim/static/notebooks/online_w2v_tutorial.html
• https://en.wikipedia.org/wiki/Smith%E2%80%93Waterman_algorithm
• http://climberg.de/page/smith-waterman-distance-for-feature-extraction-in-nlp/

13.05

• https://blog.dataiku.com/2015/08/24/xgboost_and_dss
• https://gist.github.com/walterreade/6e20dba959277bd9af77
• https://github.com/lucjb/pydata2017/blob/master/Multicolinearity.py
• https://github.com/christophebourguignat/notebooks/blob/master/Calibration.ipynb

12.05

• https://github.com/christophebourguignat/notebooks
• https://www.kaggle.com/tqchen/understanding-xgboost-model-on-otto-data#script-save-run
• http://education.parrotprediction.teachable.com/p/practical-xgboost-in-python
• https://github.com/makeyourowntextminingtoolkit/makeyourowntextminingtoolkit
• https://docs.google.com/presentation/d/1ukZMzz4rNN0MHegTNgjwLAI-kMWL1mGZPkp1bUCVckc/edit#slide=id.g21fc752465_0_74

11.05

• https://en.wikiquote.org/wiki/X_me_no_Xs#English
• https://www.ff.umb.sk/app/cmsSiteAttachment.php?ID=2348

10.05

• A/B test common pitfalls: https://www.youtube.com/watch?v=NkQ51iyFgs0

09.05

• http://www.aclweb.org/anthology/N10-1021

08.05

• https://github.com/benathi/word2gm
• http://mirnazim.org/writings/python-ecosystem-introduction/
• https://speakerdeck.com/marcobonzanini/static-type-analysis-for-robust-data-products-at-pydata-london-2017
• https://github.com/konradczechowski/discopt/blob/master/discopt_general_usage.ipynb
• high order fm: https://arxiv.org/pdf/1607.07195.pdf
• https://kaggle2.blob.core.windows.net/competitions/kddcup2012/2748/media/OperaSlides.pdf
• https://pydata.org/london2017/schedule/
• http://www.kemaswill.com/uncategorized/from-matrix-factorization-to-factorization-machines/
• https://kaggle2.blob.core.windows.net/competitions/kddcup2012/2748/media/OperaSlides.pdf
• https://github.com/geffy/tffm

05.05

• https://github.com/geffy/tffm
• ds handbook: https://github.com/jakevdp/PythonDataScienceHandbook
• https://github.com/bstriner/keras-tqdm
• https://github.com/src-d/wmd-relax
• https://github.com/krasch/presentations/blob/master/unit_testing_data_science.pdf

04.05

• https://www.kaggle.com/wangyijia/xgboost-tfidf-logloss-0-3/comments/code
• https://www.kaggle.com/jturkewitz/magic-features-0-03-gain/

03.05

• https://github.com/stared/keras-sequential-ascii
• https://github.com/abhishekkrthakur/clickbaits_revisited

02.05

• https://github.com/tjpalanca/facebook-news-analysis

30.04

• https://snap.stanford.edu/data/web-Amazon.html

27.04

• https://howchoo.com/g/ytkwotvkztq/using-the-iterm-2-and-tmux-integration

26.04

• bm25 implemention: https://github.com/alexeygrigorev/avito-duplicates-kaggle/blob/master/bm25.py
• bm25 vs tfidf: https://lettier.github.io/posts/2016-10-25-tf-idf-vsm-vs-bm25-with-vuejs.html
• https://kkulma.github.io/2017-04-24-determining-optimal-number-of-clusters-in-your-data/
• https://www.kaggle.com/c/quora-question-pairs/discussion/32069#177710
• https://www.reddit.com/r/MachineLearning/comments/67gonq/d_batch_normalization_before_or_after_relu/?st=j1y4j36m&sh=1d708b41

25.04

• https://github.com/ChenglongChen/Kaggle_CrowdFlower/tree/master/Code/Feat
• https://dnc1994.com/2016/05/rank-10-percent-in-first-kaggle-competition-en/
• https://www.slideshare.net/HJvanVeen/feature-engineering-72376750
• http://hotgram1.filmiro.com/2017/03/11/109/6118559518814109698.pdf

24.04

• http://arogozhnikov.github.io/2016/04/28/demonstrations-for-ml-courses.html
• https://github.com/Babylonpartners/fastText_multilingual
• https://github.com/pYr0rAGE/KaggleQuoraQuestionSimilarity/blob/master/notebooks/Initial%20Analysis.ipynb
• http://aylien.com/web-summit-2015-tweets-part1
• https://github.com/pksohn/tweet-clustering
• http://hdbscan.readthedocs.io/en/latest/how_hdbscan_works.html

21.04

• https://www.kaggle.com/arthurtok/titanic/introduction-to-ensembling-stacking-in-python/notebook

20.04

• https://gab41.lab41.org/batch-normalization-what-the-hey-d480039a9e3b
• https://github.com/nbgallery/nbgallery.github.io
• https://github.com/yanyang729/656_kaggle_quora_question_pair
• https://github.com/lodrice/LabelGAN
• https://github.com/bathulas/kaggle-quora/blob/master/quora.ipynb
• https://github.com/ArtistScript/Kaggle-Quora-/blob/master/kaggle/xgb.py
• https://github.com/Mustufain/Quora--Detecting-Duplicate-Questions/blob/master/Quora_Features.py
• https://github.com/codeheadshopon/Quora-Question-Pair-Classification/blob/master/SImple_Lstm_Short

19.04

• https://tryolabs.com/blog/machine-learning-deep-learning-conferences/?N
• https://gab41.lab41.org/batch-normalization-what-the-hey-d480039a9e3b
• https://gab41.lab41.org/jupyter-notebook-sharing-is-caring-5ed4831d7f71
• http://blog.smola.org/post/4110255196/real-simple-covariate-shift-correction

18.04

• http://www.abigailsee.com/2017/04/16/taming-rnns-for-better-summarization.html
• http://blog.datadive.net/selecting-good-features-part-i-univariate-selection/
• http://blog.smola.org/post/4110255196/real-simple-covariate-shift-correction
• http://www.mitpressjournals.org/doi/abs/10.1162/089976602753284446#.WPVs_VOGPdQ
• http://scikit-learn-general.narkive.com/ShZKenFK/real-simple-covariate-shift-correction-using-logistic-regression
• http://wan.poly.edu/KDD2012/docs/p168.pdf
• http://www.ml.uni-saarland.de/Publications/Hein%20-%20Binary%20Classification%20under%20Sample%20Selection%20Bias(2008).pdf
• http://www.gatsby.ucl.ac.uk/~gretton/papers/covariateShiftChapter.pdf
• SVD http://econometricsense.blogspot.com/2011/11/singular-value-decomposition-and-text.html
• Pearson vs Kendall http://www.statisticssolutions.com/correlation-pearson-kendall-spearman/
• http://www.gatsby.ucl.ac.uk/~gretton/papers/covariateShiftChapter.pdf

17.04

• df rolling http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.rolling.html
• https://github.com/DingKe/qrnn
• https://spacy.io/docs/usage/training-ner
• https://www.tensorflow.org/versions/master/api_docs/python/tf/contrib/crf/viterbi_decode

16.04

• http://llcao.net/cu-deeplearning17/project/midterm_summarize.pdf
• https://gist.github.com/stared/dfb4dfaf6d9a8501cd1cc8b8cb806d2e
• http://www.orbifold.net/default/2016/11/25/some-feedforward-neural-networks-using-keras/

15.04

• http://blog.nikhilgarg.me/2016/05/a-million-different-lives.html
• http://www.aclweb.org/anthology/W16-16

14.04

• http://blog.nikhilgarg.me/
• https://www.slideshare.net/NikhilGarg51?utm_campaign=profiletracking&utm_medium=sssite&utm_source=ssslideview
• https://qconsf.com/sf2016/system/files/presentation-slides/scaling_quality_using_machine_learning_-_qcon_sf_2016.pdf
• pydatalondon, May: https://pydata.org/london2017/schedule/
• https://github.com/airalcorn2/RankNet

13.04

• hamaru: https://arxiv.org/pdf/1704.03477.pdf
• https://github.com/maartenbreddels/ipyvolume
• https://pydata.org/amsterdam2017/schedule/presentation/11/
• https://github.com/godatadriven/risk-analysis
• https://github.com/godatadriven/pydata-2017-dsp-tutorial

12.04

• modern nlp: http://nbviewer.jupyter.org/github/skipgram/modern-nlp-in-python/blob/master/executable/Modern_NLP_in_Python.ipynb
• maxout: http://www-etud.iro.umontreal.ca/~goodfeli/maxout.html
• https://jamesmccaffrey.wordpress.com/2013/11/05/why-you-should-use-cross-entropy-error-instead-of-classification-error-or-mean-squared-error-for-neural-network-classifier-training/
• https://github.com/dmesquita/understanding_tensorflow_nn
• https://pub.uni-bielefeld.de/data
• gated non consecutive cnn: https://arxiv.org/pdf/1512.05726.pdf
• tf for baby: https://medium.freecodecamp.com/big-picture-machine-learning-classifying-text-with-neural-networks-and-tensorflow-d94036ac2274
• acl 16 workshop: http://www.aclweb.org/anthology/W16-16

10.04

• http://nbviewer.jupyter.org/github/skipgram/modern-nlp-in-python/blob/master/executable/Modern_NLP_in_Python.ipynb
• https://github.com/rykov8/ssd_keras/blob/master/SSD_training.ipynb
• https://vkolachalama.blogspot.in/2016/05/keras-implementation-of-mlp-neural.html
• best practice: https://arxiv.org/pdf/1704.01568.pdf
• https://www.slideshare.net/khomenko1/from-data-science-to-production-deploy-scale-enjoy-pydata-amsterdam-mar-12-2016
• https://github.com/gianlucahmd/loads_clustering/blob/master/loads_clustering.ipynb

08.04

• ffm: http://www.csie.ntu.edu.tw/~r01922136/slides/ffm.pdf
• https://medium.com/startup-grind/i-reverse-engineered-a-500m-artificial-intelligence-company-in-one-week-heres-the-full-story-d067cef99e1c
• http://www.learnbymarketing.com/950/winning-a-kaggle-competition-analysis/
• imbalance: https://silicon-valley-data-science.github.io/learning-from-imbalanced-classes/Gaussians.html
• https://www.svds.com/learning-imbalanced-classes/
• 3 idiots, ad prediction criteo: http://www.csie.ntu.edu.tw/~r01922136/kaggle-2014-criteo.pdf
• https://docs.google.com/presentation/d/1bte84MNQu3LDq5WjNMP3ZBDsMfn0eKlnwBvvKFBWVFI/edit#slide=id.g20276450fa_1_28
• https://medium.com/startup-grind/i-reverse-engineered-a-500m-artificial-intelligence-company-in-one-week-heres-the-full-story-d067cef99e1c

07.04

• https://gist.github.com/udibr
• tf sequence tagging: https://guillaumegenthial.github.io/sequence-tagging-with-tensorflow.html
• tweet2vec cluster: https://github.com/vendi12/tweet2vec_clustering
• learning to generate review and discore sentiment: https://github.com/openai/generating-reviews-discovering-sentiment
• https://aclweb.org/anthology/K15-1013
• https://github.com/brmson/dataset-sts
• https://drive.google.com/drive/folders/0B-btHzfJjPnobXZ0MndjSkxkRkk

06.04

• http://pasky.or.cz/cp/poster-repl4nlp2016.pdf
• https://www.quora.com/How-do-I-learn-deep-learning-in-2-months
• non-linear transformation: https://swarbrickjones.wordpress.com/2017/03/28/cross-entropy-and-training-test-class-imbalance/#more-2486
• homedepot: https://github.com/ChenglongChen/Kaggle_HomeDepot

05.04

• https://github.com/kootenpv/tweetokenize
• http://labs.septeni-technology.jp/
• pointer LSTM: https://github.com/keon/pointer-networks
• https://rare-technologies.com/text-summarization-in-python-extractive-vs-abstractive-techniques-revisited/
• https://github.com/mattilyra/glove2h5

04.04

• http://slides.com/smerity/quora-frontiers-of-memory-and-attention#/35
• https://github.com/cesc-park/CRCN/blob/master/keras/examples/kaggle_otto_nn.py
• https://www.visme.co/make-information-beautiful/dona-wong-visualizing-financial-data/
• http://web.stanford.edu/class/cs224n/reports.html
• http://www.chioka.in/differences-between-l1-and-l2-as-loss-function-and-regularization/

03.04

• https://nlp.stanford.edu/~socherr/pa4_ner.pdf
• https://github.com/chokkan/crfsuite/blob/master/example/ner.py
• https://www.reddit.com/r/MachineLearning/comments/3dz3fl/dl_architectures_for_entity_recognition_and_other/

01.04

• https://github.com/tuanavu/coursera-university-of-washington/tree/master/machine_learning/3_classification

31.03

• https://www.edge.org/response-detail/23691
• https://arxiv.org/pdf/1408.0782.pdf

30.03

• deepnl: https://github.com/attardi/deepnl
• https://gist.github.com/jeremystan/c236000a4159f9d47c28784fa6693c45#file-initial_architecture-py
• Relationship Modeling network: https://pbs.twimg.com/media/C7dvymYVQAAut9_.jpg:large
• https://tech.instacart.com/deep-learning-with-emojis-not-math-660ba1ad6cdc
• Rethink RNN: https://docs.google.com/document/d/1X9f-wst8QhrCCFTWiJIz6vq1qAOlpyYAUo_kaFf0J8M/edit
• crfasrnn: https://github.com/torrvision/crfasrnn

29.03

• silicon valley ds: https://github.com/silicon-valley-data-science/RNN-Tutorial
• https://github.com/richliao/textClassifier
• https://richliao.github.io/supervised/classification/2016/12/26/textclassifier-RNN/

28.03

• https://www.dropbox.com/s/tohrsllcfy7rch4/SimpleQuestions_v2.tgz
• https://github.com/sujitpal/dl-models-for-qa
• http://allenai.org/data.html
• https://www.nervanasys.com/building-skip-thought-vectors-document-understanding/

27.03

• https://truyentran.github.io/talks/ai16-tute-part-I.pdf
• https://github.com/truyentran
• RE with LSTM in TF: https://github.com/thunlp/TensorFlow-NRE
• http://www.exegetic.biz/blog/2015/12/making-sense-logarithmic-loss/
• http://nghiaho.com
• https://liusida.github.io/2016/10/31/translate-from-tf-2-keras/

26.03

• https://github.com/pandas-dev/pandas/blob/master/doc/cheatsheet/Pandas_Cheat_Sheet.pdf
• https://github.com/stanfordnlp/cs224n-winter17-notes/

25.03

• https://github.com/seatgeek/fuzzywuzzy
• misunderstanding of P: http://tuanvannguyen.blogspot.com/2017/03/10-hieu-lam-ve-tri-so-p-trong-khoa-hoc.html

23.03

• http://cs224d.stanford.edu/reports_2016.html
• https://github.com/hycis/bidirectional_RNN
• https://github.com/MLWave/Kaggle-Ensemble-Guide
• https://github.com/stanfordnlp/cs224n-winter17-notes
• https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/CIKM14_tutorial_HeGaoDeng.pdf

21.03

• DSSM: https://www.microsoft.com/en-us/research/project/dssm/?from=http%3A%2F%2Fresearch.microsoft.com%2Fen-us%2Fprojects%2Fdssm%2F
• MS NLP https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/CIKM14_tutorial_HeGaoDeng.pdf

20.03

• https://github.com/kweonwooj/kaggle_santander_product_recommendation
• bn in application: https://github.com/yskmt/kaggle-otto/tree/master/keras
• https://github.com/WenchenLi/kaggle/blob/master/otto/keras/kaggle_otto_nn.py
• https://github.com/ducha-aiki/caffenet-benchmark/blob/master/batchnorm.md

I haven't gone back to check what they are suggesting in their original paper, but I can guarantee that recent code written by Christian applies relu before BN. It is still occasionally a topic of debate, though.

17.03

• install keras on gpu: please use --no-deps flags: https://github.com/fchollet/keras/wiki/Keras-2.0-release-notes
• quora again: https://github.com/abhishekkrthakur/is_that_a_duplicate_quora_question
• clickbait: https://github.com/abhishekkrthakur/clickbaits_revisited

16.03

• http://www.cs.cornell.edu/courses/cs474/2005fa/Handouts/advanced-qa.pdf
• https://github.com/fchollet/keras/wiki/Keras-2.0-release-notes
• https://www.slideshare.net/anirudhkoul/squeezing-deep-learning-into-mobile-phones
• https://automatedinsights.com/blog/the-python-nlp-ccosystem-a-short-and-very-opinionated-guide
• https://metamind.io/research/learning-when-to-skim-and-when-to-read

15.03

• https://github.com/rguthrie3/DeepLearningForNLPInPytorch/blob/master/Deep%20Learning%20for%20Natural%20Language%20Processing%20with%20Pytorch.ipynb
• http://pytorch.org/#pip-install-pytorch
• tweet calendar: http://ec2-54-170-89-29.eu-west-1.compute.amazonaws.com:8000//month/201703/
• https://www.cs.cornell.edu/courses/cs6740/2010sp/
• hello keras 2: I love it, https://blog.keras.io/
• how to annotate: https://docs.google.com/document/d/1caUD8h-M117pKlds8rRP8jzQ0GN41NzD9UYvog4NyuQ/edit#heading=h.ggo1tu2159da
• social health mining: http://www.cs.jhu.edu/~mdredze/code.php
• http://www.sciencedirect.com/science/article/pii/S088523081630002X

14.03

• seq2seq on tf(general) https://github.com/google/seq2seq
• sentencepiece tokenizer https://github.com/google/sentencepiece

13.03

• visual search in es: https://github.com/tuan3w/visual_search
• 9-15% twitter active users are bot: https://arxiv.org/pdf/1703.03107.pdf
• http://www.springer.com/gp/book/9783319472409
• https://arxiv.org/pdf/1602.04427.pdf
• Socher at LXMS: http://lxmls.it.pt/2014/socher-lxmls.pdf
• use vgg to classify cat/dog: https://gist.github.com/embanner/6149bba89c174af3bfd69537b72bca74
• https://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html

10.03

• https://github.com/meereeum/lda2vec-tf
• https://github.com/DiceTechJobs/ConceptualSearch

09.03

• https://github.com/fastai/courses
• https://www.slideshare.net/0xdata/arno-candel-aibythebay-030617
• https://nodexlgraphgallery.org/Pages/Graph.aspx?graphID=98336
• https://medium.com/salesforce-engineering/salesforce-research-deep-learning-breakthroughs-d83c8b2ac4c3#.a9zswyhov

08.03

• CMU RF and control course: https://katefvision.github.io/
• https://www.slideshare.net/JasonKessler/turning-unstructured-content-into-kernels-of-ideas/52
• norvig ngram: http://norvig.com/ngrams/

07.03

• https://www.slideshare.net/JasonKessler/turning-unstructured-content-into-kernels-of-ideas/52
• https://arxiv.org/pdf/1703.00565.pdf
• https://jasonkessler.github.io/st-sim.html
• Dr Bao H.T JAIST: http://www.jaist.ac.jp/~bao/VIASM-SML/Lecture/L1-ML%20overview.pdf
• Khanh UMD: https://github.com/khanhptnk?tab=repositories

06.03

• http://campuspress.yale.edu/yw355/deep_learning/
• https://github.com/georgeiswang/Keras_Example
• https://github.com/thomasj02/DeepLearningProjectWorkflow
• https://tensorflow.github.io/serving/docker.html
• Deep learning in NLP: http://campuspress.yale.edu/yw355/deep_learning/

05.03

• fcholet: xception https://arxiv.org/pdf/1610.02357.pdf

04.03

• https://github.com/jfsantos/TensorFlow-Book
• https://github.com/jfsantos/keras-tutorial/blob/master/notebooks/5%20-%20Improving%20generalization%20with%20regularizers%20and%20constraints.ipynb

02.03

• https://explosion.ai/blog/supervised-similarity-siamese-cnn
• https://github.com/TeamHG-Memex/eli5/blob/master/README.rst
• https://github.com/cemoody/topicsne?files=1
• http://smerity.com/articles/2017/deepcoder_and_ai_hype.html

01.03

• http://smerity.com/articles/2017/deepcoder_and_ai_hype.html
• Twitter NER annotation: https://docs.google.com/document/d/12hI-2A3vATMWRdsKkzDPHu5oT74_tG0-PPQ7VN0IRaw/edit
• WNUT 19, Japan, result: https://noisy-text.github.io/2016/pdf/WNUT19.pdf
• pytorch vs keras/tf: https://www.reddit.com/r/MachineLearning/comments/5w3q74/d_so_pytorch_vs_tensorflow_whats_the_verdict_on/
• quora duplicate question detection: accuracy 1%(84.8) higher but 100x params than my model: https://github.com/abhishekkrthakur/is_that_a_duplicate_quora_question/blob/master/deepnet.py
• https://github.com/chiphuyen/tf-stanford-tutorials?files=1
• pretrained fasttext on wikipedia: https://github.com/facebookresearch/fastText

28.02

• https://github.com/uclmr/emoji2vec/blob/master/TwitterClassification.ipynb
• http://blog.outcome.io/pytorch-quick-start-classifying-an-image/
• https://blog.mariusschulz.com/2014/06/03/why-using-in-regular-expressions-is-almost-never-what-you-actually-want

27.02

• random walk -> graph -> node2vec: http://www.kdd.org/kdd2016/subtopic/view/node2vec-scalable-feature-learning-for-networks
• URL2VEC: http://www.newfoundland.nl/wp/?p=112
• 5 diseases of doing science: http://www.sciencedirect.com/science/article/pii/S104898431730070X
• recommended book: https://www.amazon.com/Language-Processing-Perl-Prolog-Implementation/
• Martin DL without PHD: https://github.com/martin-gorner/tensorflow-mnist-tutorial
• https://codelabs.developers.google.com/codelabs/cloud-tensorflow-mnist/#0
• https://docs.google.com/presentation/d/18MiZndRCOxB7g-TcCl2EZOElS5udVaCuxnGznLnmOlE/pub?slide=id.p
• https://docs.google.com/presentation/d/1TVixw6ItiZ8igjp6U17tcgoFrLSaHWQmMOwjlgQY9co/pub?slide=id.p

26.02

• https://medium.com/zendesk-engineering/how-zendesk-serves-tensorflow-models-in-production-751ee22f0f4b#.diz6kjaus
• https://github.com/gkamradt/Lessons-Learned-Data-Science-Interviews/blob/master/Lessons%20Learned%20-%20Data%20Science%20Interviews.pdf

25.02

• gensim 1.0: https://rare-technologies.com/gensim-switches-to-semantic-versioning/
• https://www.slideshare.net/AhmadQamar3/using-deep-neural-networks-for-fashion-applications

24.02

• how to init uniform (-b,b), summerschool of marek http://www.marekrei.com/blog/26-things-i-learned-in-the-deep-learning-summer-school/
• Beam preprocessing: https://research.googleblog.com/2017/02/preprocessing-for-machine-learning-with.html
• https://github.com/offbit/char-models/blob/master/doc-rnn2.py

23.02

• http://affinelayer.com/pixsrv/
• https://github.com/affinelayer/pix2pix-tensorflow#datasets-and-trained-models

22.02

• https://github.com/offbit/char-models
• https://offbit.github.io/how-to-read/
• https://hackernoon.com/learning-ai-if-you-suck-at-math-p4-tensors-illustrated-with-cats-27f0002c9b32#.xqpspe69f
• Beam search, NN tut from Quoc Le: https://cs.stanford.edu/~quocle/tutorial2.pdf
• marek sequence tagger: https://github.com/marekrei/sequence-labeler

21.02

• https://github.com/marekrei/sequence-labeler
• markrei word + char attention: http://www.marekrei.com/blog/
• datalab: https://github.com/googledatalab/
• https://tw.pycon.org/2017/en-us/speaking/cfp/

20.02

• https://github.com/ZhitingHu/logicnn
• http://www.cs.cmu.edu/~zhitingh/data/acl16harnessing_slides.pdf
• Lample: https://arxiv.org/pdf/1603.01360.pdf, https://github.com/glample/tagger
• stacked NN LSTM: https://github.com/clab/stack-lstm-ner
• https://github.com/napsternxg/DeepSequenceClassification/blob/master/model.py
• chatbot: https://github.com/Marsan-Ma/tf_chatbot_seq2seq_antilm
• keras crf https://github.com/pressrelations/keras/blob/98b2bb152b8d472150a3fc4f91396ce7f767bed9/examples/conll2000_bi_lstm_crf.py
• Ma Xue, CMU: best paper in ACL 2016, Germany https://github.com/XuezheMax/LasagneNLP
• rnn+cnn+crf: https://arxiv.org/pdf/1603.01354.pdf
• https://github.com/napsternxg/DeepSequenceClassification/blob/master/model.py
• https://github.com/pth1993/vn_spam_sms_filtering/blob/master/src/sms_filtering.py
• https://data36.com/wp-content/uploads/2016/08/practical_data_dictionary_final_data36_tomimester_published.pdf

19.02

• scikit plot: https://github.com/reiinakano/scikit-plot

18.02

• really cool Francis: https://github.com/frnsys/
• ai notes: http://frnsys.com/ai_notes/ai_notes.pdf
• brilliant wrong, ROC explanation: http://arogozhnikov.github.io/2015/10/05/roc-curve.html
• yandex MLSchool in Londo: https://github.com/yandexdataschool/MLatImperial2017/

17.02

• RNNs bag of applications: http://www.cs.toronto.edu/~urtasun/courses/CSC2541_Winter17/RNN.pdf
• BiMPM https://arxiv.org/pdf/1702.03814.pdf
• TextSum step by step: http://www.fastforwardlabs.com/luhn/
• https://keon.io/rl/deep-q-learning-with-keras-and-gym/
• https://medium.com/startup-grind/fueling-the-ai-gold-rush-7ae438505bc2#.ny8j80fl3
• big 5 for DS: https://www.quora.com/How-do-you-judge-a-good-Data-scientist-with-just-5-questions
• keon: https://github.com/keon/awesome-nlp
• quid: word2vec + wikipedia: https://quid.com/feed/how-quid-improved-its-search-with-word2vec-and-wikipedia?utm_content=42445351&utm_medium=social&utm_source=twitter
• https://gist.github.com/asmeurer/5843625

16.02

• market2vec: https://github.com/talolard/MarketVectors/blob/master/preparedata.ipynb
• anything2vec: https://gist.github.com/nzw0301/333afc00bd508501268fa7bf40cafe4e
• https://github.com/bradleypallen/keras-movielens-cf
• https://www.slideshare.net/t_koshikawa?utm_campaign=profiletracking&utm_medium=sssite&utm_source=ssslideview
• https://github.com/lipiji/App-DL
• http://www.slideshare.net/LimZhiYuanZane/deep-learning-for-stock-prediction
• https://github.com/kh-kim/stock_market_reinforcement_learning
• stock2vec: https://github.com/kh-kim/stock2vec
• deepwalk and word2vec: http://nadbordrozd.github.io/blog/2016/06/13/deepwalking-with-companies/
• http://m-mitchell.com/NAACL-2016/SemEval/SemEval-2016.pdf
• gandl: https://github.com/codekansas/gandlf
• predictive on stock trading with sentiment: http://www.kdnuggets.com/2016/01/sentiment-analysis-predictive-analytics-trading-mistake.html
• https://github.com/bradleypallen/keras-emoji-embeddings
• https://github.com/bradleypallen/keras-quora-question-pairs/blob/master/README.md
• DESM: https://www.microsoft.com/en-us/research/project/dual-embedding-space-model-desm/

15.02

• sentiment analysis on Super Bowl: http://blog.aylien.com/sentiment-analysis-of-2-2-million-tweets-from-super-bowl-51/
• spacy advanced text analysis: https://github.com/JonathanReeve/advanced-text-analysis-workshop-2017/blob/master/advanced-text-analysis.ipynb
• pytorch: https://github.com/vinhkhuc/PyTorch-Mini-Tutorials
• Quora engineering: https://engineering.quora.com/Semantic-Question-Matching-with-Deep-Learning
• Space bag of nns: https://explosion.ai/blog/quora-deep-text-pair-classification
• AUC 0.875 http://analyzecore.com/2017/02/08/twitter-sentiment-analysis-doc2vec/

14.02

• event detection, extraction, triggering, mention: https://github.com/anoperson/jointEE-NN
• batch renorm, due to sensitivity of batch size, initiation: https://arxiv.org/pdf/1702.03275.pdf
• https://github.com/bmitra-msft/Demos/blob/master/notebooks/DESM.ipynb
• nn for document ranking, mistra, ms cntk: https://github.com/bmitra-msft/NDRM
• TFDevSummit: https://events.withgoogle.com/tensorflow-dev-summit/watch-the-videos/#content

13.02

• Quora siamese: https://github.com/erogol/QuoraDQBaseline

12.02

• http://www.slideshare.net/BhaskarMitra3/neural-text-embeddings-for-information-retrieval-wsdm-2017

10.02

• kerlym: https://github.com/osh/kerlym
• ICLR 17: https://amundtveit.com/2016/11/12/deep-learning-for-natural-language-processing-iclr-2017-discoveries/
• https://github.com/spro/practical-pytorch/blob/master/seq2seq-translation/seq2seq-translation.ipynb
• all but of the top, pca on word2vec: https://arxiv.org/pdf/1702.01417.pdf
• https://github.com/peter3125/sentence2vec

08.02

• polarised term for document anonymisation: https://ddu1.github.io/Anonymization/
• oxford course: https://github.com/oxford-cs-deepnlp-2017/lectures
• tf fold: dynamic batching: https://research.googleblog.com/2017/02/announcing-tensorflow-fold-deep.html
• https://www.insight-centre.org/sites/default/files/publications/newhorizons_online.pdf
• https://github.com/chsasank/Traffic-Sign-Classification.keras/blob/master/Traffic%20Sign%20Classification.ipynb

07.02

• openrefine: http://alexpetralia.com/posts/2015/12/14/the-problem-with-openrefine-clean-vs-messy-data
• https://www.linkedin.com/pulse/keras-neural-networks-win-nvidia-titan-x-abhishek-thakur
• deep q learning with keras and gym: https://keon.io/rl/deep-q-learning-with-keras-and-gym/
• structured attention, Yoon Kim and Hoang Luong: https://github.com/harvardnlp/struct-attn
• understanding DL requires rethinking generalisation: https://openreview.net/pdf?id=Sy8gdB9xx
• GAN: https://github.com/osh/KerasGAN

06.02

• http://lxmls.it.pt/2016/LxMLS2016.pdf
• http://www.cs.umb.edu/~twang/file/tricks_from_dl.pdf
• https://svn.spraakdata.gu.se/repos/richard/pub/ml2016_web/LT2306_2016_example_solution.pdf
• https://svn.spraakdata.gu.se/repos/richard/pub/ml2015_web/l7.pdf
• https://chsasank.github.io/spoken-language-understanding.html
• ML4NLP: http://stp.lingfil.uu.se/~shaooyan/ml/nn.part2.pdf
• Topic Modeling for extracting key words: http://bugra.github.io/work/notes/2017-02-05/topic-modeling-for-keyword-extraction/
• Google Scraper: https://github.com/NikolaiT/GoogleScraper
• Richard Johanson: https://svn.spraakdata.gu.se/repos/richard/pub/ml2015_web/l7.pdf
• https://code.facebook.com/posts/457605107772545/under-the-hood-building-accessibility-tools-for-the-visually-impaired-on-facebook/
• l2svm outperforms softmax: https://arxiv.org/pdf/1306.0239v4.pdf
• xent vs hinge loss: http://cs231n.github.io/linear-classify/
• https://github.com/nzw0301/keras-examples/blob/master/Skip-gram-with-NS.ipynb
• model zoo pytorch: https://github.com/Cadene/tensorflow-model-zoo.torch
• quora question pair: http://www.forbes.com/sites/quora/2017/01/30/data-at-quora-first-quora-dataset-release-question-pairs/#3d052ef475cb
• Psychometric, CA and Trump: https://motherboard.vice.com/en_us/article/how-our-likes-helped-trump-win

27.1

• https://github.com/bbelderbos/Codesnippets/tree/master/python
• https://codelabs.developers.google.com/codelabs/tensorflow-for-poets/index.htm

26.1

• https://jaan.io/food2vec-augmented-cooking-machine-intelligence/
• http://multithreaded.stitchfix.com/blog/2017/01/23/scaling-ds-at-sf-slides-from-ddtexas/
• https://docs.docker.com/docker-for-mac/
• https://codelabs.developers.google.com/codelabs/tensorflow-for-poets/index.html#1
• https://petewarden.com/

25.1

• question duplication of Quora: https://data.quora.com/First-Quora-Dataset-Release-Question-Pairs
• stats for hackers code: https://github.com/croach/blog/tree/master/content
• http://multithreaded.stitchfix.com/blog/2017/01/23/scaling-ds-at-sf-slides-from-ddtexas/

24.1

• wordrank: http://deliprao.com/archives/124
• code: https://bitbucket.org/shihaoji/wordrank
• https://github.com/RaRe-Technologies/gensim/blob/develop/docs/notebooks/WordRank_wrapper_quickstart.ipynb
• https://github.com/RaRe-Technologies/gensim/blob/develop/docs/notebooks/WordRank_wrapper_quickstart.ipynb
• https://github.com/parulsethi/gensim/blob/wordrank_wrapper/docs/notebooks/Wordrank_comparisons.ipynb
• https://rare-technologies.com/wordrank-embedding-crowned-is-most-similar-to-king-not-word2vecs-canute/

23.1

• nlp terms for novice: http://www.datasciencecentral.com/profiles/blogs/10-common-nlp-terms-explained-for-the-text-analysis-novice?utm_content=buffer172af&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer
• blockchain: https://opendatascience.com/blog/what-is-the-blockchain-and-why-is-it-so-important/
• nbgrader: https://github.com/jupyter/nbgrader
• Adversarial ML: https://mascherari.press/introduction-to-adversarial-machine-learning/
• 4 questions for G. Hinton: https://gigaom.com/2017/01/16/four-questions-for-geoff-hinton/
• Debug in TF: https://wookayin.github.io/TensorflowKR-2016-talk-debugging/#1

20.1

• demysify DS: https://docs.google.com/presentation/d/1N3KhPA--cQNjF9mD4Z4IzjKKFdwq1Ff6wQ6NN102uIk/edit#slide=id.g1be386a8a6_0_21
• ML on mobile: http://alexsosn.github.io/ml/2015/11/05/iOS-ML.html
• https://www.bignerdranch.com/blog/use-tensorflow-and-bnns-to-add-machine-learning-to-your-mac-or-ios-app/
• https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/ios_examples
• https://github.com/dennybritz/sentiment-analysis

19.1

• Facebook again, pytorch: http://pytorch.org/
• https://rare-technologies.com/new-gensim-feature-author-topic-modeling-lda-with-metadata/
• pointer network: https://github.com/devsisters/pointer-network-tensorflow

18.1

• http://blog.dennybritz.com/2017/01/17/engineering-is-the-bottleneck-in-deep-learning-research/
• ml for practitioner: http://martin.zinkevich.org/rules_of_ml/rules_of_ml.pdf
• write dl/nn from scratch: https://github.com/dmlc/minpy

17.1

• improve headlines with salient words and seo score: http://www-personal.umich.edu/~tdszyman/misc/nlpmj16.pdf
• text summarisation: http://www-personal.umich.edu/~tdszyman/misc/summarization15.pdf
• word embedding over time: http://www-personal.umich.edu/~tdszyman/misc/InsightSIGNLP16.pdf
• victor DS politech in France: https://github.com/Vict0rSch/data_science_polytechnique
• Thien NYU: http://www.cs.nyu.edu/~thien/
• tonymooori: https://github.com/TonyMooori/studying
• learning theory: https://web.stanford.edu/class/cs229t/notes.pdf
• time series predictions: http://danielhnyk.cz/predicting-sequences-vectors-keras-using-rnn-lstm/

16.1

• Edward Dustin Tran in TF already, so cool: https://arxiv.org/pdf/1701.03757v1.pdf
• keras in tensorflow now on. @fchollet informed on Twitter.
• squeezednet = tiny alexnet (5MB) https://github.com/rcmalli/keras-squeezenet
• won $5k: https://medium.freecodecamp.com/recognizing-traffic-lights-with-deep-learning-23dae23287cc#.9yb31nsm4
• https://github.com/karoldvl/paper-2015-esc-convnet/blob/master/Code/Results.ipynb

15.1

• deep spell code: https://github.com/MajorTal/DeepSpell
• draw svg in jupyter: https://github.com/uclmr/egal
• sound classification with cnn: https://github.com/karoldvl/paper-2015-esc-convnet

14.1

• https://medium.com/@majortal/deep-spelling-9ffef96a24f6
• line bot + rnn + tf, vanhuyz: https://github.com/vanhuyz/line-sticker-bot
• https://github.com/Vict0rSch/deep_learning/tree/master/keras
• https://github.com/openai/pixel-cnn
• AWS Lambda: http://blog.matthewdfuller.com/p/aws-lambda-pricing-calculator.html
• deep text corrector: http://atpaino.com/2017/01/03/deep-text-correcter.html
• https://github.com/dhwajraj/deep-text-classifier-mtl

13.1

• convlstm: https://github.com/carlthome/tensorflow-convlstm-cell
• GAN and RNN: https://www.reddit.com/r/MachineLearning/comments/40ldq6/generative_adversarial_networks_for_text/
• generate sentences from continuous space: https://arxiv.org/pdf/1511.06349v2.pdf
• How to train your Gen. model: Sampling, likelihood or adversary

12.1

• https://www.raywenderlich.com/126063/react-native-tutorial
• ml practitioners: https://news.ycombinator.com/item?id=10954508
• spotify word2vec: https://douweosinga.com/projects/marconi?song1_id=45yEy5WJywhJ3sDI28ajTm&song2_id=
• https://github.com/DOsinga/marconi/blob/master/train_model.py
• True| Good | Kind | Useful | Relevant | Necessary https://www.quora.com/What-is-Triple-Filter-test-of-Socrates
• https://www.youtube.com/watch?v=ifYfJdo27_k
• student note: https://adeshpande3.github.io/adeshpande3.github.io/Deep-Learning-Research-Review-Week-3-Natural-Language-Processing

11.1

• ggplot2 in R: http://sharpsightlabs.com/blog/mapping-vc-investment/
• TF 1.0, mature. https://opendatascience.com/blog/rnns-in-tensorflow-a-practical-guide-and-undocumented-features/
• NN semantic encoder: https://github.com/pdasigi/neural-semantic-encoders/blob/master/nse.py
• DL in NN, overview: https://arxiv.org/pdf/1404.7828v4.pdf
• jurgen schmid: http://people.idsia.ch/~juergen/

10.1

• GDG NL: http://www.slideshare.net/RokeshJankie/introducing-tensorflow-the-game-changer-in-building-intelligent-applications
• https://github.com/ToferC/Twitter_graphing_python
• http://www.oujago.com/DL_more.html
• thiago DS at Yahoo: https://tgmstat.wordpress.com/
• deepstack playing poker: https://arxiv.org/pdf/1701.01724v1.pdf
• silly DL: https://news.ycombinator.com/item?id=13353941
• http://p.migdal.pl/2017/01/06/king-man-woman-queen-why.html
• AE for new molecule: http://www.impactjournals.com/oncotarget/index.php?journal=oncotarget&page=article&op=view&path[]=14073&pubmed-linkout=1

9.1

• xlingual embedding: https://levyomer.wordpress.com/2017/01/08/a-strong-baseline-for-learning-cross-lingual-word-embeddings-from-sentence-alignments/
• greg notebooks: https://github.com/gjreda/gregreda.com/tree/master/content/notebooks
• the periodic table of AI: http://ai.xprize.org/news/periodic-table-of-ai
• the same table of DL: http://www.deeplearningpatterns.com/doku.php/overview
• aylien text mining and analysis: Sebastien Ruder: https://arxiv.org/pdf/1609.02746v1.pdf
• DS as a freelancer from Greg Yhat: http://www.gregreda.com/2017/01/07/freelance-data-science-experience/

7.1

• how bayesian inference works: http://brohrer.github.io/how_bayesian_inference_works.html
• best vis projects in 2016: http://flowingdata.com/2016/12/29/best-data-visualization-projects-of-2016/
• https://flowingdata.com/2012/12/17/getting-started-with-charts-in-r/

5.1

• allenai biattflow: https://github.com/allenai/bi-att-flow
• fork guy: https://github.com/BinbinBian
• ICRL 17, DCNN: https://arxiv.org/pdf/1611.01604v2.pdf
• victor zhong: https://github.com/vzhong/posts-notebooks
• BN, if you wann gaussian, zero mean: https://kratzert.github.io/2016/02/12/understanding-the-gradient-flow-through-the-batch-normalization-layer.html
• statsnlp https://github.com/uclmr/stat-nlp-book
• sota of qa: http://metamind.io/research/state-of-the-art-deep-learning-model-for-question-answering/

4.1

• dynet: CMU neural networks in C++: https://github.com/clab
• systran: https://arxiv.org/pdf/1610.05540v1.pdf
• punctuation normalisation: http://www.statmt.org/wmt11/normalize-punctuation.perl
• GAN in keras: https://github.com/osh/KerasGAN
• reinforcement learning in keras and gym: https://github.com/osh/kerlym
• ML 101 for DE: https://drive.google.com/drive/folders/0B3bb7xB2VOUBMW1LQjVYUlJNRFU

3.1

• variational for text processing: https://github.com/carpedm20/variational-text-tensorflow
• spotify CNN music classification: https://www.dropbox.com/s/22bqmco45179t7z/thesis-FINAL.pdf
• kaggle winning solution for whale detection: https://github.com/benanne
• https://github.com/zygmuntz?tab=repositories

2.1.17

• overfitting in life: http://tuanvannguyen.blogspot.com/2016/12/over-fitting-va-y-nghia-thuc-te-trong.html
• optimal stopping problem: https://plus.maths.org/content/solution-optimal-stopping-problem

31.12

• visualisation NLP: http://www.aclweb.org/anthology/N16-1082

30.12

• zero shot translation: https://techcrunch.com/2016/11/22/googles-ai-translation-tool-seems-to-have-invented-its-own-secret-internal-language/

29.12

• Music Tagging, CRNN https://arxiv.org/pdf/1609.04243v3.pdf
• Benmusic: http://www.bensound.com/
• event detection: http://anthology.aclweb.org/C/C14/C14-1134.pdf

28.12

• NIPs 2016, embedding projector: https://arxiv.org/pdf/1611.05469.pdf
• stats learning: https://web.stanford.edu/class/cs229t/notes.pdf
• http://www.normansoft.com/blog/index.html
• Tf projector is really cool: https://github.com/normanheckscher/mnist-tensorboard-embeddings/blob/master/mnist_t-sne.py
• Who to follow on Twitter in ML/DL: https://twitter.com/DL_ML_Loop/lists/deep-learning-loop/members
• How to learn? BPTT https://medium.com/@karpathy/yes-you-should-understand-backprop-e2f06eab496b#.sunmvqmsx

27.12

• deep learning with Torch: https://github.com/soumith/cvpr2015
• T7: https://github.com/soumith/cvpr2015/blob/master/cvpr-torch.pdf
• GPOD general purpose object detector: https://github.com/EvgenyNekrasov/gpod
• mckinseys: http://www.forbes.com/sites/louiscolumbus/2016/12/18/mckinseys-2016-analytics-study-defines-the-future-machine-learning
• gumbel add noise to sigmoid: https://github.com/yandexdataschool/gumbel_lstm
• fastai wordembedding: https://github.com/fastai/courses/blob/master/deeplearning1/nbs/wordvectors.ipynb

26.12

• spotify cnn: http://benanne.github.io/2014/08/05/spotify-cnns.html
• Gated RNN https://arxiv.org/pdf/1612.08083v1.pdf
• http://www.slideshare.net/SebastianRuder/nips-2016-highlights-sebastian-ruder
• monolingal dataset WMT 2014: http://www.statmt.org/wmt14/translation-task.html
• neural turing machine: https://github.com/shawntan/neural-turing-machines
• yandex ml school HSE: https://github.com/yandexdataschool/HSE_deeplearning

24.12

• Laurent Dinh: Density estimation https://docs.google.com/presentation/d/152NyIZYDRlYuml5DbBONchJYA7AAwlti5gTWW1eXlLM/
• Swiftkey, LM: https://blog.swiftkey.com/swiftkey-debuts-worlds-first-smartphone-keyboard-powered-by-neural-networks/
• porting Theano to TF: https://medium.com/@sentimentron/faceoff-theano-vs-tensorflow-e25648c31800
• tractica: DL for retailer: https://www.tractica.com/automation-robotics/leveraging-deep-learning-to-improve-the-retail-experience/
• Effective Size: is Singaporean better in math than Vietnamese? if ES = 0.3, the overlap is near 90%, nothing to say in this Pisa's ranking.
• dracula: twitter POS utilised GATE: https://github.com/Sentimentron/Dracula/
• Business process with LSTM: https://arxiv.org/pdf/1612.02130v1.pdf

23.12

• https://bigdatauniversity.com/courses/deep-learning-tensorflow/

22.12

• https://quid.com/feed/how-quid-uses-deep-learning-with-small-data
• dl for coders: http://course.fast.ai/, notebooks here: https://github.com/fastai/courses
• encoder-decoder RNN: http://www.slideshare.net/ssuser77b8c6/reducing-the-dimensionality-of-data-with-neural-networks
• https://trello.com/b/rbpEfMld/data-science
• http://tuanvannguyen.blogspot.com/2016/12/yeu-to-nao-anh-huong-en-iem-pisa-2015.html

21.12

• https://github.com/napsternxg/TwitterNER
• news arxiv: https://news.google.com/newspapers?hl=en#F
• https://github.com/skillachie/binaryNLP
• https://github.com/skillachie/nlpArea51/blob/master/Financial_News_Text_Classification.ipynb
• http://www.kdnuggets.com/2016/12/machine-learning-artificial-intelligence-main-developments-2016-key-trends-2017.html

20.12

• http://opennmt.net
• neural relation extraction https://www.aclweb.org/anthology/P/P16/P16-1200.pdf
• claim classification: https://github.com/UKPLab/coling2016-claim-classification
• https://www.ukp.tu-darmstadt.de/fileadmin/user_upload/Group_UKP/publikationen/2016/2016_COLING_CG.pdf

19.12

• fasttext.zip https://arxiv.org/abs/1612.03651
• bi sequence classification: same SNLI, event detection: https://pdfs.semanticscholar.org/6f42/cb23262066b4034aba99bf674783ed6cac8b.pdf
• large scale contextual LSTM and NLP task: https://arxiv.org/pdf/1602.06291.pdf
• main advances in ML 2016, Xavier at Quora: https://www.quora.com/What-were-the-main-advances-in-machine-learning-artificial-intelligence-in-2016?

17.12

• https://github.com/jwkvam/bowtie

16.12

• tensorflow book with code: https://github.com/BinRoot/TensorFlow-Book
• trading with ML (Georgia university): https://www.udacity.com/course/machine-learning-for-trading--ud501

15.12

• deepbach: https://github.com/SonyCSL-Paris/DeepBach
• https://www.technologyreview.com/s/603137/deep-learning-machine-listens-to-bach-then-writes-its-own-music-in-the-same-style/
• http://www.nytimes.com/2016/12/14/magazine/the-great-ai-awakening.html?_r=0
• http://www.asimovinstitute.org/analyzing-deep-learning-tools-music/

14.12

• spacy vs nltk: https://gist.github.com/rschroll/61b20c41e984a963df2870cfc9e628ed
• psychometrics, precision marketing, privacy no longer: http://www.michalkosinski.com/
• 300+ ML projects from Stanford: http://cs229.stanford.edu/PosterSessionProgram.pdf
• NIPs 2016 codes: https://www.reddit.com/r/MachineLearning/comments/5hwqeb/project_all_code_implementations_for_nips_2016/
• Phased LSTM: Accelerating Recurrent Network Training for Long or Event-based Sequences: https://github.com/dannyneil/public_plstm

13.12

• NIPs summary: http://beamandrew.github.io/deeplearning/2016/12/12/nips-2016.html
• how to choose batch size: https://github.com/karpathy/char-rnn, https://svail.github.io/rnn_perf/, http://axon.cs.byu.edu/papers/Wilson.nn03.batch.pdf
• https://github.com/lmthang/thesis

12.12

• Relation classification (RC) via data augmentation: https://arxiv.org/abs/1601.03651
• broader twitter NER: http://www.slideshare.net/leonderczynski/broad-twitter-corpus-a-diverse-named-entity-recognition-resource
• sequence classification such as NER, POS: https://github.com/napsternxg/DeepSequenceClassification
• arctic captions: https://github.com/kelvinxu/arctic-captions/blob/master/alpha_visualization.ipynb
• COLING 2016 from 13 to 16 Dec, Japan: https://github.com/napsternxg/TwitterNER, http://coling2016.anlp.jp/

11.12

• SRL and RC: https://github.com/jiangfeng1124/emnlp14-semi, http://ir.hit.edu.cn/~jguo/papers/coling2016-mtlsrc.pdf
• https://blog.insightdatascience.com/nips-2016-day-3-highlights-robots-that-know-cars-that-see-and-more-1ec958896791
• http://www.newsreader-project.eu/files/2012/12/NWR-D5-2-1.pdf
• http://nlesc.github.io/UncertaintyVisualization/
• http://ixa2.si.ehu.es/nrdemo/demo.php
• http://ir.hit.edu.cn/~jguo/papers/coling2016-mtlsrc.pdf

9.12

• if then learning: https://papers.nips.cc/paper/6284-latent-attention-for-if-then-program-synthesis.pdf
• reinforcement learning: https://github.com/DanielTakeshi
• NIPS 2016: https://github.com/mphuget/NIPS2016
• https://github.com/zelandiya/KiwiPyCon-NLP-tutorial
• http://www.wrangleconf.com/apac.html
• http://cs231n.github.io/aws-tutorial/
• clickbait F1 98, AUC 99, too good too be true: https://arxiv.org/pdf/1612.01340v1.pdf
• https://arxiv.org/abs/1606.04474
• https://github.com/deepmind/learning-to-learn

8.12

• hackermath: https://github.com/amitkaps/hackermath/blob/master/talk.pdf
• tensorboard: https://www.tensorflow.org/versions/master/how_tos/embedding_viz/index.html
• embedding projector: http://projector.tensorflow.org/
• dl4nlp at ukplab, Germany: https://github.com/UKPLab/deeplearning4nlp-tutorial/tree/master/2016-11_Seminar
• Filter bubble vs Info cascading, Eli Pariser: https://www.ted.com/talks/eli_pariser_beware_online_filter_bubbles

7.12

• tidy data in pandas: http://www.jeannicholashould.com/tidy-data-in-python.html
• graph db: https://blog.grakn.ai/adding-semantics-to-graph-databases-with-mindmapsdb-part-1-82022bbb3b1c
• https://github.com/mikonapoli
• reinforcement learninghttp, open ai://people.eecs.berkeley.edu/~pabbeel/nips-tutorial-policy-optimization-Schulman-Abbeel.pdf
• meal description and food tagging: https://pdfs.semanticscholar.org/5f55/c5535e80d3e5ed7f1f0b89531e32725faff5.pdf

6.12

• rationale cnn [keras] https://github.com/bwallace/rationale-CNN
• churn analysis, f1 75%, lr, svm hinge: http://www.aaai.org/ocs/index.php/AAAI/AAAI15/paper/view/9849/9527
• thanapon noraset: https://northanapon.github.io/read/
• https://github.com/NorThanapon/adaptive_lm
• train general AI: https://openai.com/blog/universe/
• NIPS 2016 https://nips.cc/Conferences/2016/Schedule
• full ds notebook: https://github.com/donnemartin/data-science-ipython-notebooks
• Quoc Le, tut2: Autoencoder, CNN, RNN: http://ai.stanford.edu/~quocle/tutorial2.pdf
• Quoc Le, tut1: nonlinear classifier and backprop: http://ai.stanford.edu/~quocle/tutorial1.pdf
• Quoc Le, ex1: http://ai.stanford.edu/~quocle/exercise1.py
• https://alexanderdyakonov.wordpress.com/2016/12/04/сундуки-и-монеты/#more-4401

5.12

• semantic role labelings: https://blog.acolyer.org/2016/07/05/end-to-end-learning-of-semantic-role-labeling-using-recurrent-neural-networks/
• ml yearning: https://gallery.mailchimp.com/dc3a7ef4d750c0abfc19202a3/files/Machine_Learning_Yearning_V0.5_01.pdf
• stock embedding:https://medium.com/@TalPerry/deep-learning-the-stock-market-df853d139e02#.9q1d9hnai
• fast weights: https://github.com/ajarai

2.12

• https://github.com/cgpotts/cs224u

1.12

• https://gist.github.com/honnibal
• siamese lstm: https://github.com/aditya1503/Siamese-LSTM
• accuracy of lunar chinese calendar to predict baby sex http://onlinelibrary.wiley.com/doi/10.1111/j.1365-3016.2010.01129.x/abstract;
• customized keras lambda: https://gist.github.com/keunwoochoi

30.11

• rnn tricks: http://www.slideshare.net/indicods/general-sequence-learning-with-recurrent-neural-networks-for-next-ml
• data mining in action: Moscow, Russia: https://github.com/vkantor/MIPT_Data_Mining_In_Action_2016
• hypo testing, birthday effect: http://www.slideshare.net/SergeyIvanov105/birthday-effect-67829860
• LUI: linguistic UI https://medium.com/swlh/a-natural-language-user-interface-is-just-a-user-interface-4a6d898e9721
• fake news is 80% accuracy better: http://www.mallikarjunan.com/verytas/how-good-are-you-at-recognizing-satire-quiz
• nampi, spain 2017
• decode thought vector: http://gabgoh.github.io/ThoughtVectors/
• unstrained fmin: https://github.com/benfred/fmin
• neural programmer: https://github.com/tensorflow/models/tree/master/neural_programmer
• https://www.tensorflow.org/versions/master/how_tos/embedding_viz/index.html#tensorboard-embedding-visualization

29.11

• https://github.com/nyu-dl/NLP_DL_Lecture_Note
• NYU DL for NLP https://docs.google.com/document/d/1YS5QRvqMJVs9n3sK5fFjuldY7_vh42C5uUfxUGgL-Gc/edit
• http://tuanvannguyen.blogspot.com/2016/11/machine-learning-la-gi.html
• http://sebastianruder.com/cross-lingual-embeddings/
• https://docs.google.com/presentation/d/1O-Ics69y445aWuxQ_VW6SDvKT9BGl3ZXLLZDG9tUiUY/edit#slide=id.p

28.11

• event detection and deep learning: http://www.cs.nyu.edu/~thien/
• https://github.com/anoperson/NeuralNetworksForRE
• ED EE and MD with RNN and CNN: http://www.aclweb.org/anthology/P/P15/P15-2060.pdf

27.11

• http://www.slideshare.net/PyData/fang-xu-enriching-content-with-knowledge-base-by-search-keywords-and-wikidata
• https://www.mediawiki.org/wiki/Wikidata_query_service/User_Manual

26.11

• slides from mlconf sf 2016:http://www.slideshare.net/SessionsEvents/anjuli-kannan-software-engineer-google-at-mlconf-sf-2016
• http://www.slideshare.net/KenjiEsaki/kdd-2016-slide

25.11

• vo duy tin: https://github.com/duytinvo
• https://spacy.io/docs/usage/entity-recognition

24.11

• chinese NLP: https://github.com/taozhijiang/chinese_nlp
• not news: http://venturebeat.com/2016/11/23/twitter-cortex-team-loses-some-ai-researchers/
• sentihood: http://annotate-neighborhood.com/download/download.html, https://arxiv.org/pdf/1610.03771v1.pdf

23.11

Multithread in Theano:

• check your blas: https://raw.githubusercontent.com/Theano/Theano/master/theano/misc/check_blas.py
• http://deeplearning.net/software/theano/tutorial/multi_cores.html?highlight=multi%20co
• Theano/Theano#3239
• set OMP_NUM_THREADS=4 inside the notebook with env: https://www.dataquest.io/blog/jupyter-notebook-tips-tricks-shortcuts/

Debug

• torch vs theano vs tf: https://www.quora.com/Is-TensorFlow-better-than-other-leading-libraries-such-as-Torch-Theano

• debug Deep Learning: https://gab41.lab41.org/some-tips-for-debugging-deep-learning-3f69e56ea134#.1ldbphlav

• negative loss: keras-team/keras#1917

• CAP: Clustering Association Prediction, stas thinking https://www.researchgate.net/publication/310597778_Scientific_discovery_through_statistics

22.11

• stance detection: favour or against: http://isabelleaugenstein.github.io/papers/SemEval2016-Stance.pdf
• Hugo from Twitter to Google Brain, Montreal: https://techcrunch.com/2016/11/21/google-opens-new-ai-lab-and-invests-3-4m-in-montreal-based-ai-research/?sr_share=facebook
• train word2vec in gensim in good way: https://github.com/RaRe-Technologies/gensim/blob/develop/docs/notebooks/doc2vec-IMDB.ipynb

21.11

• sparql in python: https://joernhees.de/blog/tag/install/
• minhash: http://mccormickml.com/2015/06/12/minhash-tutorial-with-python-code/
• beating the kaggle easy way: http://www.ke.tu-darmstadt.de/lehre/arbeiten/studien/2015/Dong_Ying.pdf

19.11

• 10 takeaways writeup MLConf SF: https://tryolabs.com/blog/2016/11/18/10-main-takeaways-from-mlconf/
• theano summer school: https://github.com/mila-udem/summerschool2015
• gpu card for macbook pro: http://udibr.github.io/using-external-gtx-980-with-macbook-pro.html
• transfer learning using pretrained vgg, resnet for your problem: https://github.com/dolaameng/transfer-learning-lab

18.11

• wikidata sparql: https://docs.google.com/presentation/d/16HhxRH-kkxqxcyzepXT-dHrnE90yVPlfkPq3cM2UzFg/edit#slide=id.g18e33c9ee6_2_134
• unkify: https://github.com/cdg720/emnlp2016/blob/master/utils.py#L322
• http://smerity.com/articles/2016/google_nmt_arch.html

17.11

• wikidata: http://www.slideshare.net/_Emw/an-ambitious-wikidata-tutorial
• wptools: https://github.com/siznax/wptools/wiki
• google translate: https://arxiv.org/pdf/1611.04558v1.pdf
• https://arxiv.org/pdf/1611.05104v1.pdf
• https://arxiv.org/pdf/1611.01587v2.pdf

16.11

• dssm deep sem sim models: https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/wsdm2015.v3.pdf
• twitter @ Singapore: http://www.straitstimes.com/singapore/twitter-eyes-local-talent-for-singapore-data-science-team
• multiple tasks of NLP: https://arxiv.org/pdf/1611.01587v2.pdf
• QUASI RNN: https://arxiv.org/pdf/1611.01576v1.pdf

15.11

• regex learning: http://dlacombejr.github.io/2016/11/13/deep-learning-for-regex.html
• recurrent + cnn for text classification: https://github.com/airalcorn2/Recurrent-Convolutional-Neural-Network-Text-Classifier
• quiver: to view convnet layer https://github.com/jakebian/quiver
• hera: to see training progress board: https://github.com/jakebian/hera
• RAISR: Rapid and Accurate Image Super Resolution https://arxiv.org/pdf/1606.01299v3.pdf
• why is machine learning hard: http://ai.stanford.edu/~zayd/why-is-machine-learning-hard.html

14.11

• event ODSC West: https://www.odsc.com/california
• MLconf SF 12 Nov, summary: https://github.com/adarsh0806/ODSCWest/blob/master/MLConf.md
• Duy Do talk: https://speakerdeck.com/duydo/elasticsearch-for-data-engineers

13.11

• barcampsaigon 2016: some good topics on Elastic Search (Duy Do), Big Data analytics (Trieu Nguyen)
• Altair https://speakerdeck.com/jakevdp/visualization-in-python-with-altair

12.11

• Applications to explore (most of them are keras based)

• https://github.com/farizrahman4u/seq2seq

• https://github.com/farizrahman4u/qlearning4k

• https://github.com/matthiasplappert/keras-rl

• http://ml4a.github.io/guides/

• https://github.com/kylemcdonald/SmileCNN

• https://github.com/jocicmarko/ultrasound-nerve-segmentation

• https://github.com/abbypa/NNProject_DeepMask

• https://github.com/awentzonline/keras-rtst

• https://github.com/phreeza/keras-GAN

• https://github.com/jacobgil/keras-dcgan

• https://github.com/mokemokechicken/keras_npi

• https://github.com/codekansas/keras-language-modeling

11.11

• https://github.com/wiki-ai/revscoring
• Visual OCR attention: https://github.com/da03/Attention-OCR
• startup and DL: https://github.com/lipiji/App-DL
• embed + encode + attend + predict: https://explosion.ai/blog/deep-learning-formula-nlp
• HN: https://www.cs.cmu.edu/~diyiy/docs/naacl16.pdf

10.11

• https://arxiv.org/pdf/1508.06615.pdf

9.11

• ibm researcher, lda gib sampling, doc2vec: https://github.com/jhlau

8.11

• quoc le, rnn with reinforcement learning: http://openreview.net/pdf?id=r1Ue8Hcxg

7.11

• https://github.com/vinhkhuc/MemN2N-babi-python
• similarity proximity: http://www.datasciencecentral.com/profiles/blogs/comparison-between-global-vs-local-normalization-of-tweets-and
• pycon15, elastic search: https://github.com/erikrose/elasticsearch-tutorial

6.11

• https://github.com/Keats/rodent

04.11

• airbnb knowledge scale: https://medium.com/airbnb-engineering/scaling-knowledge-at-airbnb-875d73eff091#.5moos4eki
• R notebooks: http://rmarkdown.rstudio.com/r_notebooks.html
• dask: https://github.com/dask/dask
• dask vs celery: http://matthewrocklin.com/blog/work/2016/09/13/dask-and-celery
• dask in jupyperlab: https://learning.acm.org/webinar_pdfs/ChristineDoig_WebinarSlides.pdf

3.11

• https://hbr.org/resources/pdfs/hbr-articles/2016/11/the_state_of_machine_intelligence.pdf
• shallow learn: gensim + fasttext: https://github.com/giacbrd/ShallowLearn
• nn for sa: http://www.emnlp2016.net/tutorials/zhang-vo-t4.pdf

2.11

• mask bilstm: http://dirko.github.io/Bidirectional-LSTMs-with-Keras/
• https://github.com/clab/dynet_tutorial_examples
• data scientist at Facebook: https://tctechcrunch2011.files.wordpress.com/2016/11/data-scientist.png?w=80&h=60&crop=1
• reality vs expectation: https://pbs.twimg.com/media/CwNNnXfXEAIT-kY.jpg
• swagger + flask: http://michal.karzynski.pl/blog/2016/06/19/building-beautiful-restful-apis-using-flask-swagger-ui-flask-restplus/
• godfather of vis: https://pbs.twimg.com/media/CwNEBlsXYAAOm8G.jpg

1.11

• https://github.com/nhwhite212/Dealing-with-Data-Spring2016
• mongodb: http://www.wildml.com/2016/01/attention-and-memory-in-deep-learning-and-nlp/
• https://github.com/explosion/spaCy/tree/master/examples/keras_parikh_entailment
• decompositional attetion: https://arxiv.org/pdf/1606.01933v2.pdf

31.10

• attention lstm: http://www.wildml.com/2016/01/attention-and-memory-in-deep-learning-and-nlp/
• memory and attention, pointer: http://slides.com/smerity/quora-frontiers-of-memory-and-attention#/75

30.10

• https://www.oreilly.com/ideas/what-is-hardcore-data-science-in-practice?platform=hootsuite
• hybrid neural networks with memory: alex grave, Nature.

29.10

• multi-tag doc2vec: https://github.com/sindbach/doc2vec_pymongo
• https://dineshrai.herokuapp.com/posts/how-i-used-scikit-learn-to-find-good-eyes

28.10

• nyu dl4mt: https://github.com/nyu-dl/dl4mt-tutorial
• stock vis, phvu: https://github.com/phvu/misc/tree/master/stock_visualizer
• https://github.com/blue-yonder/tsfresh

27.10

• gensim in Russian at Yandex: https://www.youtube.com/watch?v=U0LOSHY7U5Q

26.10

• how to make a data vis: https://drive.google.com/drive/folders/0BxYkKyLxfsNVd0xicUVDS1dIS0k
• cntk: https://t.co/lMdjVfTKgE
• torch fast softmax: https://code.facebook.com/posts/1827693967466780/
• lua, rnn generates clickbait https://github.com/larspars/word-rnn
• https://github.com/sindbach/doc2vec_pymongo
• cntk rank 7: https://github.com/Microsoft/CNTK

25.10

• google brain, reading, notes, papers of Denny: https://github.com/dennybritz/cnn-text-classification-tf
• http://www.emnlp2016.net/tutorials/chen-liu-t3.pdf
• feature engineering: https://www.quora.com/What-are-some-best-practices-in-Feature-Engineering/answer/Claudia-Perlich?srid=cgo
• https://github.com/uclmr/stat-nlp-book
• http://varianceexplained.org/r/bayesian-ab-testing/
• to try GA in R: https://github.com/michalbrys/R-Google-Analytics

24.10

• unsup langid: http://blog.echen.me/2011/05/01/unsupervised-language-detection-algorithms/
• https://github.com/budhiraja/languageIdentification/blob/master/Identification%20of%20Language.ipynb

23.10

• xss detection: https://github.com/faizann24/XssPy
• bad url: https://github.com/faizann24/Using-machine-learning-to-detect-malicious-URLs
• pentest: https://github.com/faizann24/Resources-for-learning-Hacking

22.10

• https://github.com/songrotek/Deep-Learning-Papers-Reading-Roadmap

21.10

• longitudinal analysis: http://www.cs.odu.edu/~anwala/files/temp/archivesUnleashedHackathon/Hebdo_Twitter.html
• tracking discourse event on twitter: https://docs.google.com/presentation/d/1Q6vZdLbOS98kRLQv3hPQeXj77jO-PGdPN_CzupWOYGA/edit#slide=id.p
• mac archey: http://www.mitchchn.me/2014/os-x-terminal/
• google kb: https://developers.google.com/knowledge-graph/

20.10

• http://blog.mldb.ai/blog/posts/2016/10/deepteach/
• http://www.phontron.com/slides/bayesian-nonparametrics.pdf
• beamsearch: http://www.cs.cmu.edu/afs/cs.cmu.edu/academic/class/46927-f97/slides/Lec3/sld023.htm
• beamsearch in skipthough sequence generated: https://github.com/ryankiros/skip-thoughts/blob/master/decoding/search.py
• https://blog.heuritech.com/2015/12/01/learning-to-link-images-with-their-descriptions/
• https://github.com/proycon/pynlpl
• spacy 1.0 + keras: https://explosion.ai/blog/spacy-deep-learning-keras
• fipi: Automatized political analysis of text http://fipi-dev.elasticbeanstalk.com/news.html
• fipi github: https://github.com/felixbiessmann/fipi

18.10

• http://www.bbc.com/news/technology-37684418
• https://github.com/ogencoglu/Templates/tree/master/Python
• progressbat tqdm: https://pypi.python.org/pypi/tqdm

17.10

• text to image: thought vector: https://github.com/paarthneekhara/text-to-image

16.10

• https://github.com/Dyakonov/case_sdsj
• https://nbviewer.jupyter.org/github/Dyakonov/case_sdsj/blob/master/dj_sdsj01_visual.ipynb
• https://alexanderdyakonov.wordpress.com/2015/11/06/%D0%B7%D0%BD%D0%B0%D0%BA%D0%BE%D0%BC%D1%81%D1%82%D0%B2%D0%BE-%D1%81-pandas-%D1%81%D0%BB%D0%B0%D0%B9%D0%B4%D1%8B/
• http://blog.xukui.cn/
• https://github.com/uclmr/emoji2vec
• https://danielmiessler.com/blog/machine-learning-new-statistics/#gs.gpTYESc
• keras-team/keras#1400

15.10

• https://www.scripted.com/content-marketing-2/36-years-presidential-debates

14.10

• SemEval: http://www.saifmohammad.com/WebPages/StanceDataset.htm
• stance detection, implicitly: https://github.com/sheffieldnlp/stance-conditional
• https://github.com/transcranial/keras-js
• http://www.slideshare.net/isabelleaugenstein/weakly-supervised-machine-reading

13.10

• deep sequence classification: https://github.com/napsternxg/DeepSequenceClassification
• German NER, SENNA: https://www.ukp.tu-darmstadt.de/fileadmin/user_upload/Group_UKP/publikationen/2014/2014_GermEval_Nested_Named_Entity_Recognition_with_Neural_Networks.pdf
• fin keyword expansion: tf + tfidf + log(1+tc): https://www.aclweb.org/anthology/D/D14/D14-1152.pdf

12.10

• deep font: https://erikbern.com/2016/01/21/analyzing-50k-fonts-using-deep-neural-networks/
• David talk: https://github.com/dav009/topictalk/blob/master/slides.pdf

11.10

• 8 cat companies classified: https://davidgrosfeld.files.wordpress.com/2010/03/revenuevisualization1.png
• pandas multilevel columns: http://stackoverflow.com/questions/21443963/pandas-multilevel-column-names
• https://github.com/plouismarie/basic-sigmoid-ML

10.10

• https://github.com/ewulczyn/wiki-misc
• https://github.com/vered1986/linker?files=1
• energy consumption prediction: http://cs229.stanford.edu/proj2015/345_report.pdf
• DL stock volatility and goog trend domestic: http://cs229.stanford.edu/proj2015/186_report.pdf
• learning and mining: https://github.com/shuyo/iir
• langid, cldr, ldig: https://github.com/shuyo/ldig
• langid for tweet: http://www.aclweb.org/anthology/W14-1303
• http://www.dialog-21.ru/en/dialogue2016/results/program/day-1/
• nerc in russian: http://www.dialog-21.ru/media/3433/sysoevaaandrianovia.pdf

7.10

• wiki2vec: https://github.com/deliarusu/wikipedia-correlation/blob/master/wikipedia-correlation-ftse100.ipynb
• https://github.com/nadbordrozd/blog_stuff/tree/master/classification_w2v
• http://blog.datafox.com/the-data-driven-approach-to-finding-similar-companies/
• http://blog.ventureradar.com/2016/07/27/new-features-enhanced-similar-companies-lists-and-suggested-keywords/
• http://nadbordrozd.github.io/blog/2016/06/13/deepwalking-with-companies/

6.10

• resume job matching: https://arxiv.org/pdf/1607.07657.pdf
• https://github.com/lyoshiwo/resume_job_matching
• short text sim with word embedding: https://staff.fnwi.uva.nl/m.derijke/wp-content/papercite-data/pdf/kenter-short-2015.pdf
• yandex deeplearning school: https://github.com/yandexdataschool/HSE_deeplearning
• http://haixun.olidu.com/Probase.Feb.2014.pdf

5.10

• turin nlp4twitter: https://github.com/leoferres/nlp4twitter_tutorial
• spacy nlp: https://github.com/NSchrading/intro-spacy-nlp/blob/master/Intro_spaCy_NLP.ipynb
• twitter calendar: http://ec2-54-170-89-29.eu-west-1.compute.amazonaws.com:8000/
• https://maxdemarzi.com/2012/08/10/summarize-opinions-with-a-graph-part-1/
• http://aclanthology.info/
• https://github.com/aritter/twitter_nlp
• https://github.com/deliarusu/pydata_berlin2016_materials

3.10

• https://github.com/ijmbarr/panama-paper-network
• https://maxdemarzi.com/2012/08/10/summarize-opinions-with-a-graph-part-1/
• http://www.slideshare.net/neo4j/natural-language-processing-with-graphs
• why clt: http://www.kdnuggets.com/2016/08/central-limit-theorem-data-science-part-2.html
• wikitext: http://metamind.io/research/the-wikitext-long-term-dependency-language-modeling-dataset/
• auto headline from description: https://github.com/udibr/headlines?files=1
• https://handong1587.github.io/deep_learning/2015/10/09/nlp.html#summarization
• movies for ds: https://ongxuanhong.wordpress.com/2016/09/04/data-scientists-nen-xem-gi/
• opinosis: http://kavita-ganesan.com/opinosis

30.9

• nlp4fin: http://www3.nd.edu/~mcdonald/Word_Lists.html
• ssix: social sentiement index: http://ssix-project.eu/
• http://www3.nd.edu/~mcdonald/

29.9

• stats nlp book: https://github.com/uclmr/stat-nlp-book
• rep learning on twitter: https://github.com/cedricdeboom/RepresentationLearning
• short text rep: https://arxiv.org/pdf/1607.00570.pdf
• sa on post debation: https://www.datazar.com/project/p7dc15a26-4551-4c79-8e41-fbc7b84641fd
• https://blog.datazar.com/first-debate-2016-sentimental-analysis-of-candidates-58d87092fc6a#.iz6sgezbx

28.9

• r blogger: http://juliasilge.com/blog/Song-Lyrics-Across/
• fastText as hybrid recommender system: https://blog.lateral.io/2016/09/fasttext-based-hybrid-recommender/
• pointer sentinel mixture models: http://arxiv.org/pdf/1609.07843v1.pdf

27.9

• dl4nlp: http://web.eecs.umich.edu/~radev/intronlp/
• CommAI: https://github.com/facebookresearch/CommAI-env
• a roadmap towards ML: http://arxiv.org/pdf/1511.08130v2.pdf
• apply dl, nuts and bolts: https://kevinzakka.github.io/2016/09/26/applying-deep-learning/
• vis nlp: https://arxiv.org/pdf/1506.01066v2.pdf
• vis nn: https://civisanalytics.com/blog/data-science/2016/09/22/neural-network-visualization/

26.9

• LIWC: little words in big data: https://github.com/scottofthescience/liwcExtractor
• LIWC paper 2010, http://www.cs.cmu.edu/~ylataus/files/TausczikPennebaker2010.pdf
• TweetNLP: http://www.cs.cmu.edu/~ark/TweetNLP/
• acl 15: http://aclweb.org/anthology/P/P15/
• AKE: automatic keywords extraction for twitter: http://aclweb.org/anthology/P/P15/P15-2105.pdf
• lex comparison btw Wikipedia and Twitter corpus: http://aclweb.org/anthology/P/P15/P15-2108.pdf

25.9

• Y Lecun: DL and AI future: https://www.youtube.com/watch?v=wofXCQXq1pg&feature=youtu.be
• youtube rec: https://static.googleusercontent.com/media/research.google.com/vi//pubs/archive/45530.pdf
• lie detector: https://docs.google.com/presentation/d/1tHt5EPol3KLu81E8aKlebZT3FH0uvPky4l3bHEwVbEQ/mobilepresent?slide=id.g162ec24d83_0_0

23.9

• adam opt = sgd + momentum http://cs231n.github.io/neural-networks-3/#ada
• need4tweet: https://github.com/badiehm/TwitterNEED
• https://research.googleblog.com/2016/09/show-and-tell-image-captioning-open.html
• http://colinmorris.github.io/blog/1b-words-char-embeddings

22.9

• http://www.kdnuggets.com/2015/03/deep-learning-text-understanding-from-scratch.html
• http://www.john-foreman.com/blog
• stats: http://andrewgelman.com/
• https://github.com/ujjwalkarn/DataSciencePython
• https://wwbp.org/blog/do-the-presidential-candidates-have-a-plan-or-highlight-problems/#more-408
• http://wwbp.org/blog/insights-to-the-2016-election/

21.9

• hack password with 3LSTM + 2 Dense: https://www.usenix.org/system/files/conference/usenixsecurity16/sec16_paper_melicher.pdf
• useful R wikipediatrend: https://github.com/petermeissner/wikipediatrend

20.9

• http://deadtreepages.com/posts/python-code-character-prediction-with-lstm-rnn.html
• why is your data valuable? https://github.com/ogencoglu/MyData2016
• http://www.cs.tut.fi/kurssit/SGN-41006/slides/
• fast region-cnn: https://github.com/rbgirshick/py-faster-rcnn
• automatic text scoring: https://github.com/dimalik/ats, http://www.aclweb.org/anthology/P/P16/P16-1068.pdf
• https://www.zybuluo.com/HaomingJiang/note/462804
• model extraction attack: https://github.com/ftramer/Steal-ML
• nlp and dl from India: https://github.com/rishy?tab=repositories
• http://slides.com/rishabhshukla/deep-learning-in-nlp#/
• https://github.com/hungtraan/FacebookBot

19.9

• https://github.com/giuseppebonaccorso/Reuters-21578-Classification/blob/master/Text%20Classification.ipynb
• Cortex team, RL: https://blog.twitter.com/2016/reinforcement-learning-for-torch-introducing-torch-twrl

15.9

• altair: http://nbviewer.jupyter.org/github/ellisonbg/altair/blob/master/altair/notebooks/12-Measles.ipynb
• nice illustration: http://www.asimovinstitute.org/neural-network-zoo/
• https://github.com/shashankg7/Deep-Learning-for-NLP-Resources
• https://github.com/RaRe-Technologies/gensim/blob/develop/docs/notebooks/Word2Vec_FastText_Comparison.ipynb

14.9

• vqa: https://github.com/iamaaditya/VQA_Demo/blob/master/demo.py
• wavenet in keras: https://github.com/usernaamee/keras-wavenet
• why jupyter notebook so cool: http://arogozhnikov.github.io/2016/09/10/jupyter-features.html
• fin news aggregator: https://finzine.com/

13.9

• thought vector, Hinton again: http://deeplearning4j.org/thoughtvectors
• semantic parsing, extracting B-dep, B-arr, B-date from airline info: http://deeplearning.net/tutorial/rnnslu.html
• deep learning intro in Taiwan: http://mail.tku.edu.tw/myday/teaching/1042/SCBDA/1042SCBDA09_Social_Computing_and_Big_Data_Analytics.pdf
• tankbuster: https://github.com/thiippal/tankbuster/blob/master/README.md
• trending arxiv: https://github.com/Smerity/trending_arxiv

9.9

• pydataberlin 2016: https://github.com/deeplook/pydata_berlin2016_materials
• wavenet: https://deepmind.com/blog/wavenet-generative-model-raw-audio/

8.9

• use vgg for image classification: https://gist.github.com/fchollet/7eb39b44eb9e16e59632d25fb3119975
• guide to phd from Karpathy: http://karpathy.github.io/2016/09/07/phd/
• image captioning, thesis of Karpathy: http://cs.stanford.edu/people/karpathy/main.pdf
• step data in Iphone: http://blog.yhat.com/posts/phone-steps-timeseries.html

7.9

• movidius VPU: http://www.forbes.com/sites/aarontilley/2016/01/27/google-partners-with-chip-startup-to-take-machine-learning-out-of-the-cloud-and-into-your-pocket/?linkId=20717947#6c21345a33e5
• google news 3M to 300K, slim version: https://github.com/eyaler/word2vec-slim

6.9

• source code classification: http://blog.aylien.com/source-code-classification-using-deep-learning/
• aaqi ml for text, sound: https://github.com/aqibsaeed
• data science at Google: http://www.unofficialgoogledatascience.com/2015/11/how-to-get-job-at-google-as-data.html
• is your model stat significantly improved? https://www.reddit.com/r/MachineLearning/comments/519z41/when_would_i_say_that_my_classifier_performance/

5.9

• learn to learn: http://nuit-blanche.blogspot.com/2016/09/saturday-morning-videos-learning-to.html
• big data and stochastic algos: http://www.proba.jussieu.fr/SlidesAlgoSto/AlgoStoch_Gallinari.pdf
• random bits on data: https://datathinking.wordpress.com/author/dolaameng/
• hitchhiker guide to the galaxy: http://izt.ciens.ucv.ve/ecologia/Archivos/Filosofia-II/Adams,%20Douglas%20-%20The%20Hitchhikers%20Guide%20To%20The%20Galaxy.pdf
• deep learning 42 https://www.youtube.com/watch?v=furfdqtdAvc&feature=youtu.be
• optimization algos in Haskell
• learn to learn and decompositionality with deep nn: https://www.youtube.com/watch?v=x1kf4Zojtb0

2.9

• sum product networks: https://arxiv.org/abs/1608.08266
• keras application, used inception v3: https://github.com/danielvarga/keras-finetuning
• keras: neural style: https://github.com/titu1994/Neural-Style-Transfer

1.9

• pseudo topic modeling PTM: http://www.kdd.org/kdd2016/subtopic/view/topic-modeling-of-short-texts-a-pseudo-document-view
• cucumber classification: https://cloud.google.com/blog/big-data/2016/08/how-a-japanese-cucumber-farmer-is-using-deep-learning-and-tensorflow
• implemented bigru on tweet2vec: https://arxiv.org/pdf/1605.03481v2.pdf
• deep learning for computing vision, Barcelona, Spain 2016: http://imatge-upc.github.io/telecombcn-2016-dlcv/

31.8

• doc2text: https://github.com/jlsutherland/doc2text
• synthetic gradient: https://deepmind.com/blog#decoupled-neural-interfaces-using-synthetic-gradients
• Oriol vanyals, pioneer in s2s got 35 under 35 award: https://www.technologyreview.com/lists/innovators-under-35/2016/pioneer/oriol-vinyals/
• https://github.com/baidu/paddle

29.8

• text to image: https://github.com/paarthneekhara/text-to-image/blob/master/skipthoughts.py
• forex prediction UP, DOWN, MID: https://is.muni.cz/th/422802/fi_b/bakalarka_final.pdf

28.8

• keras + lasagne: https://github.com/Vict0rSch/deep_learning/tree/master/keras/recurrent
• last week: pydatachi2016, pyconmy2016
• https://github.com/y3l2n/pydata2016-notes
• sebastian raschka, ml tut: https://github.com/rasbt/pydata-chicago2016-ml-tutorial/blob/master/code/tutorial.ipynb

26.8

• euroscipy with kera: https://github.com/leriomaggio/deep-learning-keras-euroscipy2016/
• deep mask from Facebook

25.8

• lstm vis: http://lstm.seas.harvard.edu/
• ML at Apple: https://backchannel.com/an-exclusive-look-at-how-ai-and-machine-learning-work-at-apple-8dbfb131932b#.asw5moh3s
• https://research.googleblog.com/2016/08/text-summarization-with-tensorflow.html
• https://ujjwalkarn.me/2016/08/11/intuitive-explanation-convnets/

24.8

• http://benjaminbolte.com/blog/2016/keras-language-modeling.html
• https://github.com/julienr/ipynb_playground/blob/master/keras/convmnist/keras_cnn_mnist.ipynb
• http://swanintelligence.com/first-steps-with-neural-nets-in-keras.html
• https://groups.google.com/forum/#!topic/keras-users/9C5emrJRRUs

23.8

• https://www.aclweb.org/aclwiki/index.php?title=State_of_the_art
• http://jmlr.org/proceedings/papers/v48/johnson16.pdf

22.8

• https://webcourse.cs.technion.ac.il/232601/Spring2016/en/ho.html
• http://www.deeplearningbook.org/contents/guidelines.html
• http://mickypaganini.github.io/

19.8

• sensity analysis for sentence classification: http://arxiv.org/pdf/1510.03820v4.pdf
• https://code.facebook.com/posts/1438652669495149/fair-open-sources-fasttext
• https://github.com/dolaameng/deeplearning-exploration

18.8

• https://www.springboard.com/blog/data-science-interviews-lessons/
• https://medium.com/autonomous-agents/how-to-train-your-neuralnetwork-for-wine-tasting-1b49e0adff3a#.5dzpfqoyz

16.8

• http://www.wangzhongyuan.com/tutorial/ACL2016/Understanding-Short-Texts/
• make your pip: http://marthall.github.io/blog/how-to-package-a-python-app/

15.8

• https://github.com/poliglot/fasttext/blob/master/README.md
• fasttext python: https://pypi.python.org/pypi/fasttext/0.6.1

14.8

• https://github.com/dgrtwo/JSM2016slides
• http://nlpers.blogspot.com/2016/08/fast-easy-baseline-text-categorization.html
• https://github.com/fchollet/keras/blob/master/examples/imdb_fasttext.py

13.8

• sequence to poetry: http://arxiv.org/pdf/1511.06349.pdf
• http://adilmoujahid.com/posts/2016/06/introduction-deep-learning-python-caffe/
• http://www.cs.toronto.edu/~hinton/absps/NatureDeepReview.pdf
• http://techcitynews.com/2016/08/11/tech-giants-embracing-artificial-intelligence/
• https://code.facebook.com/posts/181565595577955/introducing-deeptext-facebook-s-text-understanding-engine/

12.8

• sentiment analysis, alway good things from Chris Pott: http://sentiment.christopherpotts.net/index.html
• wwbp: https://penncurrent.upenn.edu/2016-03-24/latest-news/penn-scholars-distinguish-presidential-candidates-analyzing-their-words/

10.8

• http://varianceexplained.org/r/trump-tweets/
• http://koaning.io/theme/iframes/ams-meetup-notebook.html

9.8

• short text understanding http://sci-hub.cc/10.1109/tkde.2015.2485224
• https://github.com/johnymontana/nlp-graph-notebooks/blob/master/opinion_mining.ipynb
• world well being project http://wwbp.org/blog/
• fastText benchmark: https://github.com/jayantj/gensim/blob/683720515165a332baed8a2a46b6711cefd2d739/docs/notebooks/Word2Vec_FastText_Comparison.ipynb
• wp bot: https://techcrunch.com/2016/08/05/robots-will-cover-the-olympics-for-the-washington-post/
• http://datascientistjobinterview.com/
• https://hackerlists.com/tensorflow-resources/

8.8

• twitter buy magic pony: $150M, apple buy Turi $200: time of ML
• spotify release rader: brand new music from acoustic frame

7.8

• https://sites.google.com/site/acl16nmt/
• roc curve vs pr curve: http://numerical.recipes/CS395T/lectures2008/17-ROCPrecisionRecall.pdf
• https://ru.coursera.org/learn/vvedenie-mashinnoe-obuchenie/lecture/P9Zun/mnoghoklassovaia-klassifikatsiia
• hpv: https://github.com/lukaselmer/hierarchical-paragraph-vectors
• nlp pointers: https://gist.github.com/mattb/3888345
• vowpal wabbit: https://github.com/hal3/vwnlp

5.8

• I am a data scientist: https://yanirseroussi.com/2016/08/04/is-data-scientist-a-useless-job-title/
• Generative modeling, OpenAI, Ilya Sutskever: http://scaledml.org/2016/slides/ilya.pdf
• HOT: fasttext from FB: https://github.com/facebookresearch/fastText
• OMG, data leak, how to have trick on ID, time series: https://alexanderdyakonov.wordpress.com/2016/07/27/id-и-время/

4.8

• tweep in tsne: https://github.com/abhshkdz/tsne-top-indian-tweeps
• dl is not a hamer: I try ngram + tfidf to beat cnn on AG corpus: http://arxiv.org/pdf/1509.01626v3.pdf
• fasttext in torch: https://github.com/kemaswill/fasttext_torch
• super cool: self driving with open source http://research.comma.ai/
• coreNLP: http://www.lewisgavin.co.uk/NLP/

3.8

• categorize tumblr post: https://engineering.tumblr.com/post/148350944656/categorizing-posts-on-tumblr
• https://github.com/jayantj/w2vec-similarity
• https://jayantj.github.io/posts/project-gutenberg-word2vec
• reuter on keras + gensim: http://www.bonaccorso.eu/2016/08/02/reuters-21578-text-classification-with-gensim-and-keras/

2.8

• trendminer: http://www.trendminer-project.eu/index.php/downloads
• https://sites.sas.upenn.edu/danielpr/pages/resources
• https://github.com/danielpreotiuc/textrank
• text rank: https://web.eecs.umich.edu/~mihalcea/papers/mihalcea.emnlp04.pdf
• hierarchy doc2vec: http://e-collection.library.ethz.ch/eserv/eth:48203/eth-48203-01.pdf
• probase roadmap: http://tcci.ccf.org.cn/summersch/classm/ADL32-Lecture03-Report3.pdf
• understanding short text: http://www.wangzhongyuan.com/tutorial/ACL2016/Understanding-Short-Texts/
• http://tech.marksblogg.com/airflow-postgres-redis-forex.html
• keras: text + image --> http://cbonnett.github.io/Insight.html
• lstm viz: https://arxiv.org/pdf/1506.01066v2.pdf

1.8

• hacker math for DS: https://github.com/amitkaps/hackermath
• word2vec from theo to prac: http://hen-drik.de/pub/Heuer%20-%20word2vec%20-%20From%20theory%20to%20practice.pdf
• twitter job/occupation: http://www.lampos.net/sites/default/files/papers/twitterJobs_ACL15.pdf
• https://www.clarifai.com/#demo
• word dominant: http://compling.hss.ntu.edu.sg/courses/hg7017/pdf/word2vec%20and%20its%20application%20to%20wsd.pdf
• word2vec, tsne, d3js: https://github.com/h10r/topic_comparison_tool
• organic tweet: http://arxiv.org/pdf/1505.04342v6.pdf

28.7

• https://ireneli.eu/2016/05/17/nlp-04-an-log-linear-model-for-tagging-task-python/
• lexvec: https://github.com/alexandres/lexvec

27.7

• yandex data school (some in Russian): https://github.com/yandexdataschool/mlhep2016
• https://github.com/vladsandulescu/topics
• https://github.com/vladsandulescu/phrases

26.7

• caffe: http://adilmoujahid.com/posts/2016/06/introduction-deep-learning-python-caffe/
• neo4j: https://github.com/johnymontana/graph-of-thrones/blob/master/network-of-thrones.ipynb
• doc classification with DL: http://home.iitk.ac.in/~amlan/cs671/project/slides.pdf
• https://oshearesearch.com/index.php/2016/07/01/mnist-generative-adversarial-model-in-keras/
• beyond cosine: http://stefansavev.com/blog/beyond-cosine-similarity/
• disco twitter: http://stanford.edu/~rezab/papers/disco.pdf
• dl glossary: http://www.wildml.com/deep-learning-glossary/
• http://www.wildml.com/2015/12/implementing-a-cnn-for-text-classification-in-tensorflow/
• textacy: https://github.com/bdewilde/pygotham_2016/blob/master/pygotham_2016.pdf

25.7

• https://github.com/rouseguy/europython2016_dl-nlp

24.7

• http://stefansavev.com/blog/beyond-cosine-similarity/
• http://blog.keras.io/using-pre-trained-word-embeddings-in-a-keras-model.html

22.7

• http://blog.kaggle.com/2016/07/21/approaching-almost-any-machine-learning-problem-abhishek-thakur/

21.7

• clustering measurement: https://www.youtube.com/watch?v=Mf6MqIS2ql4, http://www.slideshare.net/PyData/christian-henning-assessing-the-quality-of-a-clustering
• pandas tut: https://github.com/jonathanrocher/pandas_tutorial
• hdbscan: https://www.youtube.com/watch?v=AgPQ76RIi6A&index=19&list=PLYx7XA2nY5Gf37zYZMw6OqGFRPjB1jCy6
• python app, flask --> flexx: https://www.youtube.com/watch?v=kIPL3N2Xk_c&index=22&list=PLYx7XA2nY5Gf37zYZMw6OqGFRPjB1jCy6
• pcap, feature engineering, sklearn for security: https://www.youtube.com/watch?list=PLYx7XA2nY5Gf37zYZMw6OqGFRPjB1jCy6&v=0KXfRGD-Ins
• sentiment https://github.com/lab41/sunny-side-up
• radim: http://rare-technologies.com/sigir2016_169.pdf

20.7

• can we predict https://gab41.lab41.org/can-word-vectors-help-predict-whether-your-chinese-tweet-gets-censored-711e7682d12f#.n0670sw5j
• dynamicCNN: https://github.com/FredericGodin/DynamicCNN
• SIGIR2016: http://nlp.stanford.edu/~manning/talks/SIGIR2016-Deep-Learning-NLI.pdf
• embedding metrics: https://github.com/julianser/hed-dlg-truncated/blob/master/Evaluation/embedding_metrics.py

19.7

• design and experiment https://github.com/juanshishido/experiments-guide
• fluent python notebook: https://github.com/juanshishido/fluent-python-notebooks
• yahoo text categorization: https://github.com/juanshishido/text-classification
• OKCupid: https://www.youtube.com/results?sp=SBTqAwA%253D&q=scipy+2016
• https://github.com/juanshishido/okcupid
• topic coherence: http://rare-technologies.com/validating-gensims-topic-coherence-pipeline/
• weighted random generation: http://eli.thegreenplace.net/2010/01/22/weighted-random-generation-in-python/
• nlp to ir: http://www.mitpressjournals.org/doi/pdf/10.1162/COLI_a_00239
• nlu icml 2015:http://icml.cc/2015/tutorials/icml2015-nlu-tutorial.pdf
• bayesian time series: http://icml.cc/2015/tutorials/BayesianTimeSeries.pdf

18.7

• tf on skflow: https://medium.com/@ilblackdragon/tensorflow-tutorial-part-2-9ffe47049c92#.x1s9r6nmh
• plot with point: http://walkerke.github.io/geog30323/slides/eda-2/#/20
• Bayesian Poisson Tensor Factorization: http://arxiv.org/abs/1506.03493
• dirichlet process mixture model: https://github.com/hannawallach/dpmm
• scikit tensor: https://github.com/mnick/scikit-tensor
• bptf: https://github.com/aschein/bptf
• icml 2015 tutorial: http://videolectures.net/icml2015_lille/

15.7

• jupyter lab: https://github.com/jupyter/jupyterlab
• intro image cnn: https://github.com/rouseguy/scipyUS2016_dl-image/tree/master/notebooks
• intro nlp rnn: https://github.com/rouseguy/intro2deeplearning/tree/master/notebooks
• https://speakerdeck.com/chdoig/scaling-ds-in-python
• http://blog.jupyter.org/2016/07/14/jupyter-lab-alpha/?utm_content=buffere6cf2&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer

14.7

data science summit:

• http://conf.turi.com/2016/us/agenda_day2/
• http://www.kdnuggets.com/2016/07/data-science-summit-2016-9-talks.html
• http://multithreaded.stitchfix.com/blog/2016/07/13/conf-talks-summer2016/

daily

• supervised algo empirical study: http://www.cs.cornell.edu/~caruana/ctp/ct.papers/caruana.icml06.pdf
• data driven modeling: http://jakehofman.com/ddm/2012/02/lecture-03-2/
• word embedding for fun and profit: https://speakerdeck.com/tmylk/word-embeddings-for-fun-and-profit-with-gensim-pydata-london-2016
• #scipy2016: https://speakerdeck.com/astrofrog/python-3-for-scientists
• @DataSciSummit: 12, 13 July San Francisco: http://conf.turi.com
• https://github.com/chdoig/dss-scaling-tutorial
• pokemon location in spreadsheet: https://docs.google.com/spreadsheets/d/1G8U44ukuPdM9OfpHZt3kiUYYPrnxaRccLMXvxvEvMiI/edit#gid=869820177
• nbflow: https://github.com/jhamrick/nbflow
• http://www.jeannicholashould.com/the-theorem-every-data-scientist-should-know-2.html
• tweet2news: https://sites.google.com/site/engmathstwitternews/home
• meetup document classification: http://www.meetup.com/Big-Data-Israel/events/232569748/
• build tool: http://scons.org/
• tweet lda to aspect: http://sci-hub.cc/10.1007/978-3-642-37401-2_35
• tensorflow: deep and wide: https://www.tensorflow.org/versions/r0.9/tutorials/wide_and_deep/index.html
• http://opensource.datacratic.com/mtlpy50/
• http://colah.github.io/posts/2015-01-Visualizing-Representations/

13.7

• tf practice: https://www.tensorflow.org/versions/r0.9/tutorials/wide_and_deep/index.html
• wide and deep together: https://research.googleblog.com/
• sklearn + dask: http://jcrist.github.io/dask-sklearn-part-1.html
• image: dl framework: https://pbs.twimg.com/media/ClAUr5EUkAA9--0.jpg:large
• image: knowledge: https://pbs.twimg.com/media/CifCYeSUUAArriB.jpg:large
• numpy, scipy and pandas way to go: https://plot.ly/~empet/13902/numpy-cluster-in-the-network-of-python-packages/
• rnn live stream, character image: https://www.youtube.com/watch?v=wSpPJtenw_c
• big data 5cent https://pbs.twimg.com/media/CnMAV2XXgAAkBJY.jpg
• google research: https://plus.google.com/+ResearchatGoogle/posts
• deep learning word cloud: https://pbs.twimg.com/media/CnLJLdVXYAEpN6w.jpg:large
• jupyter on rpi: http://makeyourownneuralnetwork.blogspot.de/2016/03/ipython-neural-networks-on-raspberry-pi.html

12.7

• http://mghassem.mit.edu/insights-word2vec/
• home depot: search relevance: https://github.com/ChenglongChen/Kaggle_HomeDepot/blob/master/Doc/Kaggle_HomeDepot_Turing_Test.pdf
• sentiment http://districtdatalabs.silvrback.com/modern-methods-for-sentiment-analysis
• reading tea leaves: https://www.umiacs.umd.edu/~jbg/docs/nips2009-rtl.pdf
• https://www.linkedin.com/pulse/putting-semantic-representational-models-test-tf-idf-k-means-parsa?forceNoSplash=true

11.7

• gensim phrase: http://www.markhneedham.com/blog/2015/02/12/pythongensim-creating-bigrams-over-how-i-met-your-mother-transcripts/
• xgboost out of the box: https://www.quora.com/What-machine-learning-approaches-have-won-most-Kaggle-competitions/answer/Ben-Hamner?srid=cgo&share=45a4f6de
• ES part 2: http://insightdataengineering.com/blog/elasticsearch-core/
• GAN: https://www.youtube.com/watch?v=deyOX6Mt_As

8.7

• lsa + classification: https://github.com/chrisjmccormick/LSA_Classification
• perturbation + adversarial lstm https://arxiv.org/pdf/1605.07725v1.pdf
• I don't drink tiger (beer) confused neural transalation lisa: http://104.131.78.120/
• always love Mikolov related: fastText https://arxiv.org/abs/1607.01759
• Explaining the classisfier: https://www.youtube.com/watch?v=hUnRCxnydCc
• LIME: https://github.com/marcotcr/lime
• https://artistdetective.wordpress.com/2016/06/15/how-to-teach-a-computer-common-sense/
• 6 cons, top uni + org: http://www.marekrei.com/blog/analysing-nlp-publication-patterns/
• https://github.com/ijmbarr/panama-paper-network/blob/master/panama_network.ipynb

7.7

• https://engineers.sg/conference/pyconsg2016
• https://speakerdeck.com/tmylk/americas-next-topic-model?slide=6
• http://aclweb.org/anthology/J93-1003

6.7

• machine learning done wrong: http://dataskeptic.com/epnotes/machine-learning-done-wrong.php
• https://archive.org/details/twitterstream
• https://github.com/lintool/twitter-tools
• CLT: http://www.jeannicholashould.com/the-theorem-every-data-scientist-should-know.html
• https://blog.init.ai/three-impactful-machine-learning-topics-at-icml-2016-465be5ae63a#.yxw5wiisw
• http://www.machinedlearnings.com/2016/07/icml-2016-thoughts.html?spref=tw&m=1

5.7

• w2v writeup: https://blog.acolyer.org/2016/04/21/the-amazing-power-of-word-vectors/

4.7

• bot: http://52bots.tumblr.com/post/108322694954/11-ebook-of-black-earth-what-an-ebooks-style
• http://www.degeneratestate.org/posts/2016/Apr/20/heavy-metal-and-natural-language-processing-part-1/
• http://aclweb.org/anthology/J93-1003
• https://www.thefinancialist.com/man-vs-machine-what-happens-when-machines-can-learn-2/
• https://twimlai.com/fatal-ai-autopilot-crash-eu-may-prohibit-machine-learning-twiml-20160701/
• https://www.coursera.org/learn/natural-language-processing

1.7

• word2vec pipeline: https://github.com/NIHOPA/pipeline_word2vec
• visual recognition: http://cs231n.github.io/
• chris olah cv: https://colah.github.io/cv.pdf
• Google ML tut: https://www.youtube.com/watch?v=cSKfRcEDGUs
• ES: anatomy: http://insightdataengineering.com/blog/elasticsearch-crud/
• https://research.googleblog.com/2016/03/train-your-own-image-classifier-with.html
• http://arxiv.org/pdf/1512.00567v3.pdf

30.6

• voice detection: wav --> features --> SVM + RF + XGB --> RF --> prediction: http://www.primaryobjects.com/2016/06/22/identifying-the-gender-of-a-voice-using-machine-learning/
• wide and deep: https://research.googleblog.com/2016/06/wide-deep-learning-better-together-with.html
• https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/learn/python/learn

29.6

• hyperword: https://bitbucket.org/omerlevy/hyperwords
• wmd 20newsgroup: http://vene.ro/blog/word-movers-distance-in-python.html
• init orthogonal mat for RNN: http://smerity.com/articles/2016/orthogonal_init.html
• markov chain: https://github.com/fatiherikli/markov-chain-demo
• https://medium.com/@bjacobso/is-it-brunch-time-ffe3adf485d8#.sx662tn0x
• singapore statup eco: https://docs.google.com/document/d/1AsSwH_kJ5qm7X8Pb5H2P_iaCpUA9tr-93GGNuN-orXk/mobilebasic?pli=1
• http://www.datameer.com/company/datameer-blog/big-data-ecosystem/
• https://www.facebook.com/notes/bui-hai-an/v%C3%A0i-l%E1%BB%9Di-khuy%C3%AAn-thu-nh%E1%BA%B7t-%C4%91%C6%B0%E1%BB%A3c-t%E1%BB%AB-ges-p2/10153489287901106
• https://www.facebook.com/notes/bui-hai-an/v%C3%A0i-l%E1%BB%9Di-khuy%C3%AAn-thu-nh%E1%BA%B7t-%C4%91%C6%B0%E1%BB%A3c-t%E1%BB%AB-ges-p1/10153478669101106?notif_t=like&notif_id=1467086253182050

28.6

• http://lstm.seas.harvard.edu/
• https://www.reddit.com/r/MachineLearning/comments/4q5fsu/advanced_word_embeddings_for_seq2seq_applications/
• https://www.dataquest.io/blog/data-science-newsletters/
• http://nbviewer.jupyter.org/github/taddylab/deepir/blob/master/w2v-inversion.ipynb
• http://www.pyimagesearch.com/2016/06/27/my-top-9-favorite-python-deep-learning-libraries/

27.6

• document classification: https://github.com/RaRe-Technologies/movie-plots-by-genre
• classical nlp: https://github.com/tmylk/pycon-2016-nlp-tutorial/blob/master/jupyter/classical-nlp/classical-nlp.ipynb
• document classification: https://speakerdeck.com/tmylk/document-classification-with-word2vec-at-pydata-nyc
• inverse word2vec with hs: http://nbviewer.jupyter.org/github/taddylab/deepir/blob/master/w2v-inversion.ipynb
• wmd: http://tech.opentable.com/2015/08/11/navigating-themes-in-restaurant-reviews-with-word-movers-distance/
• defense of w2v: http://www.cs.tau.ac.il/~wolf/papers/qagg.pdf
• plagiarism: http://douglasduhaime.com/blog/cross-lingual-plagiarism-detection-with-scikit-learn
• genre stereotype in word embedding: https://arxiv.org/pdf/1606.06121v1.pdf
• twitter intent: https://twitter.com/intent/user?user_id=328567812
• google n-gram: https://books.google.com/ngrams/graph?content=she+is+a+nurse%2C+he+is+a+nurse&year_start=1800&year_end=2000&corpus=15&smoothing=3&share=&direct_url=t1%3B%2Cshe%20is%20a%20nurse%3B%2Cc0%3B.t1%3B%2Che%20is%20a%20nurse%3B%2Cc0
• FE techniques in simple words: https://codesachin.wordpress.com/2016/06/25/non-mathematical-feature-engineering-techniques-for-data-science/
• Money laundering detection: http://conf.startup.ml/blog/aml
• bias embedding: http://nlpers.blogspot.hr/2016/06/language-bias-and-black-sheep.html

24.6

• keynote: http://tpq.io/p/pyconsg.html#/
• customer segmentation: https://github.com/maoting1223/pycon_sg_2016
• https://github.com/mirri66/geodata

23.6

• I'm a speaker at pyconsg 2016: https://pycon.sg/schedule/
• https://github.com/airbnb/caravel
• googlenet: 22 layers inception http://arxiv.org/abs/1409.4842

22.6

• http://varianceexplained.org/r/year_data_scientist/
• conflicted ds: https://www.youtube.com/watch?v=7h2S3eM1OYQ&feature=youtu.be
• ICML 2016: http://icml.cc/2016/?page_id=1839
• My russian friends: https://alexanderdyakonov.wordpress.com/2016/05/31/avito-telstra-bnp/
• https://www.youtube.com/watch?v=1HrkBzLBJQg

21.6

• book of Andrew Ng: http://www.mlyearning.org/
• why we need so many classifiers: http://jmlr.org/papers/volume15/delgado14a/delgado14a.pdf
• variety of models to choose: https://www.quora.com/What-are-the-advantages-of-different-classification-algorithms

20.6

• deephack, https://drive.google.com/file/d/0B0PX5JnpNX8yQ184Z3kwWmYyQUU/view?pref=2&pli=1
• mikilov rnn + scrnn: https://drive.google.com/file/d/0B0PX5JnpNX8yQ184Z3kwWmYyQUU/view?pref=2&pli=1

18.6

• deep hack: http://deepqa.tilda.ws/page78823.html
• Quoc Le, DL for lang understanding: https://www.youtube.com/watch?v=KmOdBS4BXZ0
• https://drive.google.com/file/d/0B0PX5JnpNX8yQ184Z3kwWmYyQUU/view?pref=2&pli=1
• https://drive.google.com/file/d/0BwJbEyAV32gETHR4YmdjcW5JUlU/view?pref=2&pli=1

17.6

• http://www.cs.utexas.edu/~roller/naacl2016dl.txt

16.6

toread:

• relationship modeling network https://github.com/miyyer/rmn

• QA compose NN: http://arxiv.org/pdf/1601.01705v4.pdf

• gensim 0.13 changelog: https://github.com/RaRe-Technologies/gensim/blob/develop/CHANGELOG.txt

• https://building-babylon.net/2015/06/03/document-embedding-with-paragraph-vectors/

• gbt http://nbviewer.jupyter.org/github/ogrisel/notebooks/blob/master/sklearn_demos/Gradient%20Boosting.ipynb

• https://docs.google.com/presentation/d/1KOvkwjZM1Wjj7hfbBP11fLrSUYJTPl_zaAM-fZje328/edit?pref=2&pli=1#slide=id.gd894eccf5_0_75

• https://foxtype.com/sentence-tree

15.6

• map 140M tweet: http://www.mapd.com/demos/tweetmap/
• http://blog.yhat.com/posts/rodeo-2.0-release.html

13.6

• personality: https://personality-insights-livedemo.mybluemix.net/
• imbalanced data https://github.com/ngaude/kaggle/blob/master/cdiscount/ImbalancedLearning.pdf
• just remember: https://ipgp.github.io/scientific_python_cheat_sheet/
• work on postgres https://github.com/dbcli/pgcli
• productionize with Kafka: http://blog.parsely.com/post/3886/pykafka-now/

11.6

• xgboost + nn: https://www.import.io/post/how-to-win-a-kaggle-competition/

9.6

user classifiers:

• http://www.slideshare.net/TedXiao/winning-kaggle-101-dmitry-larkos-experiences
• Humanizr: http://networkdynamics.org/resources/software/humanizr/
• tweet coder: http://networkdynamics.org/resources/software/tweetcoder/
• latent user, delip rao: http://www.cs.jhu.edu/~delip/smuc.pdf

Readings:

• intro prob in ipython: http://nbviewer.jupyter.org/url/norvig.com/ipython/Probability.ipynb
• thesis learning algos from data: http://www.cs.nyu.edu/media/publications/zaremba_wojciech.pdf
• Practical tools for exploring data and models: http://had.co.nz/thesis/practical-tools-hadley-wickham.pdf

8.6

• deep learning demo: http://www.somatic.io/models/V7Zx4Z9A
• ml debug: https://www.quora.com/Whats-the-best-way-to-debug-natural-language-processing-code-How-do-we-know-its-running-as-we-assume-I-ask-this-question-because-I-read-one-post-titled-as-what-is-the-best-way-to-test-machine-learning-code-I-am-working-on-one-natural-language-processing-task-and-has-confusion-on-how-to-debug-NLP
• confusion matrix: http://www.innoarchitech.com/machine-learning-an-in-depth-non-technical-guide-part-4/
• nlp ml error analysis tool: http://www.aclweb.org/anthology/C14-2001
• deepnet online: https://github.com/anujgupta82/DeepNets/blob/master/Online_Learning/Incorporating_feedback_in_DeepNets.ipynb
• sgd + elasticnet penalty better? https://www.quora.com/Are-there-any-real-applications-of-using-Elastic-Net
• http://cs231n.github.io/linear-classify/
• binary classification dog vs cat: http://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html
• deeptext: am I scared? https://www.linkedin.com/pulse/what-listening-look-facebooks-ai-engine-radim-%C5%99eh%C5%AF%C5%99ek
• why Google open source Tensorflow: https://www.youtube.com/watch?v=Rnm83GqgqPE

7.6

• top 10 NLP con: http://idibon.com/the-top-10-nlp-conferences/
• metricle: https://metricle.com/api
• dark web svm, word2vec: https://homepages.staff.os3.nl/~delaat/rp/2014-2015/p99/report.pdf
• M$ deep learning: http://research.microsoft.com/pubs/246721/NAACL-HLT-2015_tutorial.pdf
• MS sgd trick: http://research.microsoft.com/pubs/192769/tricks-2012.pdf
• 10K class and 10M samples, OVR and SGD is best: https://hal.inria.fr/hal-00835810/PDF/TPAMI_minor_revision.pdf

6.6

• xin rong presentation: https://www.youtube.com/watch?v=D-ekE-Wlcds
• wevi: https://docs.google.com/presentation/d/1yQWN1CDWLzxGeIAvnGgDsIJr5xmy4dB0VmHFKkLiibo/
• stats for hacker pycon2016: https://speakerdeck.com/pycon2016/jake-vanderplas-statistics-for-hackers
• https://medium.com/udacity/this-week-in-machine-learning-3-june-2016-7f089ce984e7#.zbv7h9nyo
• word galaxy: http://www.anthonygarvan.com/wordgalaxy/
• pycon2016: https://github.com/singingwolfboy/build-a-flask-api
• http://burhan.io/flask-web-api-with-firebase/
• http://web.stanford.edu/class/cs224u/materials/cs224u-vsm-overview.pdf

1.6

• lab41: http://www.lab41.org/a-tour-of-sentiment-analysis-techniques-getting-a-baseline-for-sunny-side-up/
• doc2vec at tripadvisor: https://github.com/hellozeyu/An-advisor-for-TripAdvisor
• https://nycdatascience.com/an-advisor-for-tripadvisor/

10 lesson learned from Xavier recap:

• implicit signal beats explicit ones (almost always): clickbait, rating psychology
• your model will learn what you teach it to learn: feature, function, f score
• sup + unsup = life
• everything is ensemble
• model sequences: output of the model is input of others
• FE: reusable, transformable, interpretable, reliable
• ML infra: experimentation phase: easiness, flexibility, reusability. production phase: performance, scalable
• Debugging feature values
• you don't need to distribute ML algo
• DS + ML engineering = perfection

31.5

• pycon2016: https://www.youtube.com/channel/UCwTD5zJbsQGJN75MwbykYNw
• andreas, intro ML/sklearn for DS: https://github.com/amueller/introduction_to_ml_with_python
• Berkeley ds intro: https://data-8.appspot.com/sp16/course

30.5

• dirichlet process: http://stiglerdiet.com/blog/2015/Jul/28/dirichlet-distribution-and-dirichlet-process/
• pycon 2016: https://github.com/justmarkham/pycon-2016-tutorial/
• romance in word2vec: http://www.ghostweather.com/files/word2vecpride/
• topic quality coherence: http://palmetto.aksw.org/palmetto-webapp/
• https://spacy.io/docs
• https://spacy.io/docs/tutorials/twitter-filter
• http://sebastianraschka.com/Articles/2014_naive_bayes_1.html
• https://github.com/justmarkham/pycon-2016-tutorial

29.5

• cry analysis: http://www.robinwe.is/explorations/cry.html
• spacy preprocessing: https://github.com/cemoody/lda2vec/blob/master/lda2vec/preprocess.py
• spacy Tweet: https://spacy.io/docs/tutorials/twitter-filter
• lda2vec: full http://multithreaded.stitchfix.com/blog/2016/05/27/lda2vec/#topic=38&lambda=1&term=
• probalistic approach: http://chirayukong.github.io/infsci2725/resources/09_Probabilistic_Approaches.pdf
• lda curation: https://datawarrior.wordpress.com/2016/04/20/local-and-global-words-and-topics/
• why hdbscan: http://nbviewer.jupyter.org/github/lmcinnes/hdbscan/blob/master/notebooks/Comparing%20Clustering%20Algorithms.ipynb
• auto ml: http://papers.nips.cc/paper/5872-efficient-and-robust-automated-machine-learning.pdf
• http://www.kdnuggets.com/2016/05/five-machine-learning-projects-cant-overlook.html
• topic2vec: https://www.cs.cmu.edu/~diyiy/docs/naacl15.pdf

26.5

• http://alexperrier.github.io/jekyll/update/2015/09/04/topic-modeling-of-twitter-followers.html
• http://alexperrier.github.io/jekyll/update/2015/09/16/segmentation_twitter_timelines_lda_vs_lsa.html
• https://begriffs.com/posts/2015-03-10-better-tweets-datascience.html
• https://github.com/alexperrier/datatalks/tree/master/twitter
• https://issuu.com/andriusknispelis/docs/topic_models_-_video
• http://www.aclweb.org/anthology/W15-1526
• https://www.opendatascience.com/blog/dissecting-the-presidential-debates-with-an-nlp-scalpel/
• https://speakerdeck.com/bmabey/visualizing-topic-models

25.5

In summary, here is what I recommend if you plan to use word2vec: choose the right training parameters and training data for word2vec, use avg predictor for query, sentence and paragraph(code here) after picking a dominant word set and apply deep learning on resulted vectors.

===

For SGNS, here is what I believe really happens during the training: If two words appear together, the training will try to increase their cosine similarity. If two words never appear together, the training will reduce their cosine similarity. So if there are a lot of user queries such as “auto insurance” and “car insurance”, then “auto” vector will be similar to “insurance” vector (cosine similarity ~= 0.3) and “car” vector will also be similar to “insurance” vector. Since “insurance”, “loan” and “repair” rarely appear together in the same context, their vectors have small mutual cosine similarity (cosine similarity ~= 0.1). We can treat them as orthogonal to each other and think them as different dimensions. After training is complete, “auto” vector will be very similar to “car” vector (cosine similarity ~= 0.6) because both of them are similar in “insurance” dimension, “loan” dimension and “repair” dimension. This intuition will be useful if you want to better design your training data to meet the goal of your text learning task.

===

for short sentences/phrases, Tomas Mikolov recommends simply adding up individual vector words to get a "sentence vector" (see his recent NIPS slides).

For longer documents, it is an open research question how to derive their representation, so no wonder you're having trouble :)

I like the way word2vec is running (no need to use important hardware to process huge collection of text). It's more usable than LSA or any system which requires a term-document matrix.

Actually LSA requires less structured data (only a bag-of-words matrix, whereas word2vec requires exact word sequences), so there's no fundamental difference in input complexity.

• http://douglasduhaime.com/blog/clustering-semantic-vectors-with-python
• https://www.kaggle.com/c/word2vec-nlp-tutorial/details/part-3-more-fun-with-word-vectors
• http://bookworm.benschmidt.org/posts/2015-10-25-Word-Embeddings.html
• http://www.aclweb.org/anthology/W15-1526
• ML model: http://blog.echen.me/2011/04/27/choosing-a-machine-learning-classifier/

24.5

• https://github.com/edburnett/twitter-text-python
• http://eng.kifi.com/from-word2vec-to-doc2vec-an-approach-driven-by-chinese-restaurant-process/
• https://www.insight-centre.org/sites/default/files/publications/14.033_insight-snow14dc-final.pdf
• http://rali.iro.umontreal.ca/rali/sites/default/files/publis/Atefeh_et_al-2013-Computational_Intelligence-2.pdf

TSNE:

• javascript: http://karpathy.github.io/2014/07/02/visualizing-top-tweeps-with-t-sne-in-Javascript/
• http://cs.stanford.edu/people/karpathy/tsnejs/index.html

Conferences:

• word2vec tree: https://github.com/pvthuy/word2vec-visualization
• flask, api, mongo, d3: http://adilmoujahid.com/posts/2015/01/interactive-data-visualization-d3-dc-python-mongodb/
• https://github.com/RaRe-Technologies/movie-plots-by-genre
• wmd: http://vene.ro/blog/word-movers-distance-in-python.html
• word2vec viz: https://ronxin.github.io/wevi/
• news analytics in finance: https://vimeo.com/67901816
• table2vec: http://www.slideshare.net/SparkSummit/using-data-science-to-transform-opentable-into-delgado-das
• data by the bay: http://data.bythebay.io/schedule.html
• pydataberlin: http://pydata.org/berlin2016/

20.5

• scatter with images: https://gist.github.com/lukemetz/be6123c7ee3b366e333a

19.5

• wise 203 classes, vocab = 300k, sample = 64k, test = 34j=k, http://alexanderdyakonov.narod.ru/wise2014-kaggle-Dyakonov.pdf
• yelp review to multi label: food, deal, ambience,... http://www.ics.uci.edu/~vpsaini/
• instagram: http://instagram-engineering.tumblr.com/post/117889701472/emojineering-part-1-machine-learning-for-emoji
• emoji embedding http://www.danielforsyth.me/nba-twitter-emojis-and-word-embeddings/
• tweetmap in websummit event: http://blog.aylien.com/post/133931414053/analyzing-tweets-from-web-summit-2015-semantic
• topic2vec: http://arxiv.org/pdf/1506.08422.pdf
• http://googleresearch.blogspot.com/2016/05/chat-smarter-with-allo.html
• https://en.wikipedia.org/wiki/Limited-memory_BFGS

18.5

• building data processing at budget: http://www.slideshare.net/GaelVaroquaux/building-a-cuttingedge-data-processing-environment-on-a-budget

• https://radimrehurek.com/gensim/wiki.html

• calibration: http://scikit-learn.org/stable/auto_examples/calibration/plot_calibration_curve.html#example-calibration-plot-calibration-curve-py

• http://glowingpython.blogspot.com/2014/02/terms-selection-with-chi-square.html

• which feature selection: http://sebastianraschka.com/faq/docs/feature_sele_categories.html

• which learning algos: http://sebastianraschka.com/faq/docs/best-ml-algo.html

• for intepretability use tree: http://sebastianraschka.com/faq/docs/model-selection-in-datascience.html

• LR vs NB: http://sebastianraschka.com/faq/docs/naive-bayes-vs-logistic-regression.html

• yelp review classifier: https://github.com/parulsingh/FlaskAppCS194

• ngsg is not mf yet: https://building-babylon.net/2016/05/12/skipgram-isnt-matrix-factorisation/

• http://blog.aylien.com/post/133931414053/analyzing-tweets-from-web-summit-2015-semantic

• http://aylien.com/web-summit-2015-tweets-part1

sentifi:

• https://github.com/bdhingra/tweet2vec
• tweet2vec https://arxiv.org/abs/1605.03481
• syntaxnet: https://github.com/tensorflow/models/tree/master/syntaxnet
• hijack compromise user account http://www.icir.org/vern/papers/twitter-compromise.ccs2014.pdf
• user classification: name + loc http://www.cs.jhu.edu/~vandurme/papers/broadly-improving-user-classfication-via-communication-based-name-and-location-clustering-on-twitter.pdf
• chrispot: http://sentiment.christopherpotts.net/tokenizing.html
• https://github.com/cbuntain/TwitterFergusonTeachIn
• mining tweet: https://rawgit.com/ptwobrussell/Mining-the-Social-Web-2nd-Edition/master/ipynb/html/Chapter%201%20-%20Mining%20Twitter.html
• NE: https://noisy-text.github.io/pdf/WNUT10.pdf
• tokenizer: http://sentiment.christopherpotts.net/code-data/happyfuntokenizing.py
• twitter tokenizer online: http://sentiment.christopherpotts.net/tokenizing/results/
• cs224u understanding nlp: http://nbviewer.jupyter.org/github/cgpotts/cs224u/
• https://spacy.io/blog/german-model?utm_source=News&utm_campaign=87a64aae50-German_release_newsletter&utm_medium=email&utm_term=0_89ad33e698-87a64aae50-64293797
• jupyter theme: http://sherifsoliman.com/2016/01/11/theming-ipython-jupyter-notebook/
• noisy text need to be normalized: https://noisy-text.github.io/norm-shared-task.html
• understanding user profile/twitter: https://blog.twitter.com/2015/guest-post-understanding-users-through-twitter-data-and-machine-learning
• word2vec with numba: https://d10genes.github.io/blog/2016/05/03/word2vec/
• analyzing text data at Firefox: http://web.stanford.edu/~rjweiss/public_html/MozFest2013/
• pretrained word2vec https://github.com/3Top/word2vec-api
• twitter music word2vec: http://www.netbase.com/blog/understanding-beliebers-word2vec-twitter/
• text + images with CNN: https://www.scribd.com/doc/305710656/Convolutional-Neural-Networks-for-Multimedia-Sentiment-Analysis
• feature pivot: http://www.hpl.hp.com/techreports/2011/HPL-2011-98.pdf
• nlp with cnn: http://www.slideshare.net/devashishshanker/deep-learning-for-natural-language-processing
• event detection http://www.hpl.hp.com/techreports/2011/HPL-2011-98.pdf
• http://www.zdnet.com/article/big-data-what-to-trust-data-science-or-the-bosss-sixth-sense/
• tf is winning: https://medium.com/@mjhirn/tensorflow-wins-89b78b29aafb#.6lebzwbyx
• a vc blog: http://avc.com
• hijacking: http://www.icir.org/vern/papers/twitter-compromise.ccs2014.pdf
• us president prediction: http://www.aioptify.com/predictinguselection.php
• https://thestack.com/world/2015/05/08/three-steps-to-building-a-twitter-driven-trading-bot/
• http://file.scirp.org/pdf/SN_2015070917142293.pdf
• tweet latent attributes: http://boingboing.net/2014/09/01/twitter-uses-an-algorithm-to-f.html
• user gender inference: http://www.aclweb.org/anthology/W14-5408
• https://blog.bufferapp.com/the-5-types-of-tweets-to-keep-your-buffer-full-and-your-followers-engaged
• classifying user latent attributes: http://www.cs.jhu.edu/~delip/smuc.pdf
• http://myownhat.blogspot.com/
• http://bugra.github.io/work/notes/2015-01-17/mining-a-vc/
• NER with w2v, 400M tweet: http://www.fredericgodin.com/software/

http://davidrosenberg.github.io/ml2016/#home

pydatalondon 2016:

• http://www.thetalkingmachines.com
• https://www.youtube.com/user/PyDataTV
• pymc: https://docs.google.com/presentation/d/1QNxSjDHJbFL7vFwQHHheeGmBHEJAo39j28xdObFY6Eo/edit#slide=id.gdfcfebc22_0_118
• https://github.com/springcoil/PyDataLondonTutorial/blob/master/deck-17.pdf
• https://speakerdeck.com/bargava/introduction-to-deep-learning
• https://github.com/rouseguy/intro2deeplearning/
• https://github.com/rouseguy/intro2stats
• https://github.com/kylemcdonald/SmileCNN
• https://github.com/springcoil/PyDataLondonTutorial/blob/master/notebooks/Statistics.ipynb
• http://greenteapress.com/complexity/thinkcomplexity.pdf
• http://matthewearl.github.io/2016/05/06/cnn-anpr/

spotify:

• http://www.slideshare.net/AndySloane/machine-learning-spotify-madison-big-data-meetup
• http://www.slideshare.net/erikbern/music-recommendations-mlconf-2014

lda asyn, auto alpha: http://rare-technologies.com/python-lda-in-gensim-christmas-edition/

mapk: https://github.com/benhamner/Metrics/tree/master/Python/ml_metrics

ilcr2016: https://tensortalk.com/?cat=conference-iclr-2016

l.m.thang

• https://github.com/lmthang/nlm
• http://nlp.stanford.edu/~lmthang/data/papers/iclr16_multi.pdf

https://github.com/jxieeducation/DIY-Data-Science

http://drivendata.github.io/cookiecutter-data-science/

http://ofey.me/papers/sparse_ijcai16.pdf

Spotify:

• https://github.com/mattdennewitz/playlist-to-vec
• http://wonder.fm/
• https://social.shorthand.com/huntedguy/3CfQA8mj2S/playlist-harvesting

skflow:

• https://medium.com/@ilblackdragon/tensorflow-tutorial-part-1-c559c63c0cb1#.7a7s8tkke
• https://medium.com/@ilblackdragon/tensorflow-tutorial-part-2-9ffe47049c92#.jgxmezy95
• https://medium.com/@ilblackdragon/tensorflow-tutorial-part-3-c5fc0662bc08#.2d22an1xp
• http://terrytangyuan.github.io/2016/03/14/scikit-flow-intro/
• https://libraries.io/github/mhlr/skflow
• https://github.com/tensorflow/tensorflow/tree/master/tensorflow/examples

a few useful things to know about ML:

• https://blog.bigml.com/2013/02/15/everything-you-wanted-to-know-about-machine-learning-but-were-too-afraid-to-ask-part-one/
• https://blog.bigml.com/2013/02/21/everything-you-wanted-to-know-about-machine-learning-but-were-too-afraid-to-ask-part-two/

tdb: https://github.com/ericjang/tdb

dask for task parallel, delayed: http://dask.pydata.org/en/latest/examples-tutorials.html

skflow:

• pip install git+git://github.com/tensorflow/skflow.git
• http://www.kdnuggets.com/2016/02/scikit-flow-easy-deep-learning-tensorflow-scikit-learn.html

http://www.wildml.com/2016/04/deep-learning-for-chatbots-part-1-introduction/

https://medium.com/a-year-of-artificial-intelligence/lenny-2-autoencoders-and-word-embeddings-oh-my-576403b0113a#.ecj0iv4n8

https://github.com/andrewt3000/DL4NLP/blob/master/README.md

tf:

• http://terryum.io/ml_applications/2016/04/25/TF-Code-Structure/
• http://www.slideshare.net/tw_dsconf/tensorflow-tutorial

tf chatbot: https://github.com/nicolas-ivanov/tf_seq2seq_chatbot

• deep inversion : https://github.com/TaddyLab/gensim/blob/deepir/docs/notebooks/deepir.ipynb
• encoder decoder with attention: http://arxiv.org/pdf/1512.01712v1.pdf
• keras tut: http://web.cs.hacettepe.edu.tr/~aykut/classes/spring2016/bil722/tutorials/keras.pdf

Bayesian Opt: https://github.com/fmfn/BayesianOptimization/blob/master/examples/visualization.ipynb

click-o-tron rnn: http://clickotron.com auto generated headline clickbait: https://larseidnes.com/2015/10/13/auto-generating-clickbait-with-recurrent-neural-networks/

http://blog.computationalcomplexity.org/2016/04/the-master-algorithm.html http://jyotiska.github.io/blog/posts/python_libraries.html

LSTM: http://iamtrask.github.io/2015/11/15/anyone-can-code-lstm/

CS224d:

• TF intro: http://cs224d.stanford.edu/lectures/CS224d-Lecture7.pdf
• RNN: http://cs224d.stanford.edu/lectures/CS224d-Lecture8.pdf

Sota of sa, mikolo and me :)

• http://arxiv.org/pdf/1412.5335.pdf
• https://github.com/mesnilgr/iclr15/tree/master/scripts

Thang M. L: http://web.stanford.edu/class/cs224n/handouts/cs224n-lecture16-nmt.pdf

CS224d reports:

• classify online forum answer/non-answer: https://cs224d.stanford.edu/reports/AbajianAaron.pdf
• gender classification: https://cs224d.stanford.edu/reports/BartleAric.pdf
• job prediction: https://cs224d.stanford.edu/reports/BoucherEric.pdf
• text sum: https://cs224d.stanford.edu/reports/ChaiElaina.pdf
• email spam: https://cs224d.stanford.edu/reports/EugeneLouis.pdf
• jp2en: https://cs224d.stanford.edu/reports/GreensteinEric.pdf
• improve PV: https://cs224d.stanford.edu/reports/HongSeokho.pdf
• twitter sa: https://cs224d.stanford.edu/reports/YuanYe.pdf
• yelp sa: https://cs224d.stanford.edu/reports/YuApril.pdf
• author detector: https://cs224d.stanford.edu/reports/YaoLeon.pdf
• IMDB to Yelp: https://cs224d.stanford.edu/reports/XingMargaret.pdf
• Reddit: https://cs224d.stanford.edu/reports/TingJason.pdf
• Quora: https://cs224d.stanford.edu/reports/JindalPranav.pdf

QA in keras:

• https://github.com/avisingh599/visual-qa/blob/master/scripts/trainMLP.py
• https://avisingh599.github.io/deeplearning/visual-qa/

Chinese LSTM + word2vec:

• https://github.com/taozhijiang/chinese_nlp/blob/master/DL_python/dl_segment_v2.py
• https://github.com/taozhijiang/chinese_nlp

DL with SA: https://cs224d.stanford.edu/reports/HongJames.pdf

MAB:

• mab book: http://pdf.th7.cn/down/files/1312/bandit_algorithms_for_website_optimization.pdf
• yhat: http://blog.yhat.com/posts/the-beer-bandit.html
• test significance with AB, conversation rate opt with MAB: https://vwo.com/blog/multi-armed-bandit-algorithm/
• when to use multiarmed bandits: http://conversionxl.com/bandit-tests/
• multibandit: http://stevehanov.ca/blog/index.php?id=132

cnn nudity detection: http://blog.clarifai.com/what-convolutional-neural-networks-see-at-when-they-see-nudity/#.VxbdB0xcSko

sigopt: https://github.com/sigopt/sigopt_sklearn

first contact with TF: http://www.jorditorres.org/first-contact-with-tensorflow/

eval of ML using A/B or multibandit: http://blog.dato.com/how-to-evaluate-machine-learning-models-the-pitfalls-of-ab-testing

how to make mistakes in Python: www.oreilly.com/programming/free/files/how-to-make-mistakes-in-python.pdf

keras tut: https://uwaterloo.ca/data-science/sites/ca.data-science/files/uploads/files/keras_tutorial.pdf

Ogrisel word embedding: https://speakerd.s3.amazonaws.com/presentations/31f18ad0522c0132b9b662e7bb117668/Word_Embeddings.pdf

Tensorflow whitepaper: http://download.tensorflow.org/paper/whitepaper2015.pdf

Arimo distributed tensorflow: https://arimo.com/machine-learning/deep-learning/2016/arimo-distributed-tensorflow-on-spark/

Best ever word2vec in code: http://nbviewer.jupyter.org/github/fbkarsdorp/doc2vec/blob/master/doc2vec.ipynb

TF japanese: http://www.slideshare.net/yutakashino/tensorflow-white-paper

TF tut101: https://github.com/aymericdamien/TensorFlow-Examples

Jeff Dean: http://learningsys.org/slides/NIPS-Learning-Systems-Workshop-TensorFlow-Jeff-Dean.pdf DL: http://www.thoughtly.co/blog/deep-learning-lesson-1/ Distributed TF: https://www.tensorflow.org/versions/r0.8/how_tos/distributed/index.html

playground: http://playground.tensorflow.org/

Hoang Duong blog: http://hduongtrong.github.io/ Word2vec short explanation: http://hduongtrong.github.io/2015/11/20/word2vec/

ForestSpy: https://github.com/jvns/forestspy/blob/master/inspecting%20random%20forest%20models.ipynb

• keras for mnist: https://github.com/wxs/keras-mnist-tutorial/blob/master/MNIST%20in%20Keras.ipynb
• lasagne installation https://martin-thoma.com/lasagne-for-python-newbies/

Netflix:

• http://www.wired.com/2012/04/netflix-prize-costs/
• http://www.wired.com/2009/09/bellkors-pragmatic-chaos-wins-1-million-netflix-prize/

Lessons learned

• http://machinelearningmastery.com/lessons-learned-building-machine-learning-systems/
• http://techjaw.com/2015/02/11/10-machine-learning-lessons-harnessed-by-netflix/
• https://medium.com/@xamat/10-more-lessons-learned-from-building-real-life-ml-systems-part-i-b309cafc7b5e#.klowhfq10

WMD:

• word mover distance: https://github.com/mkusner/wmd
• gensim wmd: https://speakerdeck.com/tmylk/same-content-different-words

Hanoi trip:

• tensorflow scan: learn the cum sum https://nbviewer.jupyter.org/github/rdipietro/tensorflow-notebooks/blob/master/tensorflow_scan_examples/tensorflow_scan_examples.ipynb

• https://jayantj.github.io/posts/project-gutenberg-word2vec

• stacking: http://rasbt.github.io/mlxtend/user_guide/classifier/StackingClassifier/

• Learn and think like human: http://arxiv.org/pdf/1604.00289v1.pdf

• predictive modeling + AI: https://speakerd.s3.amazonaws.com/presentations/30ad41b99258471f9485118f904f8cfb/predictive_modeling_and_deep_learning.pdf

• sklearn vs tf: https://github.com/rasbt/python-machine-learning-book/blob/master/faq/tensorflow-vs-scikitlearn.md

• advances in DL for NLP: http://cs.nyu.edu/~zaremba/docs/Advances%20in%20deep%20learning%20for%20NLP.pdf

• Xavier, 10 lessons learned: https://medium.com/@xamat/10-more-lessons-learned-from-building-real-life-ml-systems-part-i-b309cafc7b5e#.klowhfq10

• pizza analysis: http://yoavz.com/potd/

• R at airbnb: https://medium.com/airbnb-engineering/using-r-packages-and-education-to-scale-data-science-at-airbnb-906faa58e12d#.deo3t37vr

• 450 hours in data science: http://studiy.co/path/data-science/

• LR + SGD + FM: https://gist.github.com/kalaidin/9ea737ad771fcf073e57

• libFM: http://www.ics.uci.edu/~smyth/courses/cs277/papers/factorization_machines_with_libFM.pdf

• intro FM: http://www.slideshare.net/0x001/intro-to-factorization-machines

• fastFM: https://github.com/ibayer/fastFM

• winning data science competition: https://speakerdeck.com/datasciencela/jeong-yoon-lee-winning-data-science-competitions-data-science-meetup-oct-2015

• python for data analyst: https://www.kevinsheppard.com/images/0/09/Python_introduction.pdf

• risk modeling: https://risk-engineering.org/static/PDF/slides-stat-modelling.pdf

• pyfm: https://github.com/coreylynch/pyFM

• mlss2014: http://www.mlss2014.com/materials.html

• xavier: https://www.slideshare.net/slideshow/embed_code/key/gt6HuUzZ4Z7flf

• Pedro: http://www.thetalkingmachines.com/blog/

• Machine Intelligence 2.0: https://cdn-images-1.medium.com/max/2000/1*A9exqeQ69XjjSJgMyDEo6Q.jpeg

• Quora - all about data scientits: https://www.quora.com/What-are-the-best-blogs-for-data-scientists-to-read

• World of though vector: http://www.pamitc.org/cvpr15/files/lecun-20150610-cvpr-keynote.pdf

• newbie nlp lab: https://github.com/piskvorky/topic_modeling_tutorial/

• why and when log-log is used: http://www.forbes.com/sites/naomirobbins/2012/01/19/when-should-i-use-logarithmic-scales-in-my-charts-and-graphs/#41c6dc0c3cd8

• lzma: https://parezcoydigo.wordpress.com/2011/10/09/clustering-with-compression-for-the-historian/

• Tom Vincent: http://insightdatascience.com/blog/tom_vincent_qanda.html

• Normalized Compression Distance: http://tamediadigital.ch/2016/03/20/normalized-compression-distance-a-simple-and-useful-method-for-text-clustering-2/

• Yoav Goldberg: https://www.youtube.com/watch?v=xw5HL5h1wxY

• Sklearn production on Dato: https://www.youtube.com/watch?v=AwjeRg1u5VI

VinhKhuc:

• how many k for CV: k = N e.g. LOOCV http://vinhkhuc.github.io/2015/03/01/how-many-folds-for-cross-validation.html
• backprop http://vinhkhuc.github.io/2015/03/29/backpropagation.html
• qa bAbI task: https://github.com/vinhkhuc/MemN2N-babi-python
• lstm/rnn: http://vinhkhuc.github.io/2015/11/19/rnn-lstm.html

RS:

• https://code.facebook.com/posts/861999383875667/recommending-items-to-more-than-a-billion-people/
• http://bugra.github.io/work/notes/2014-04-19/alternating-least-squares-method-for-collaborative-filtering/

Data science bootcamp: https://cambridgecoding.com/datascience-bootcamp#outline

CambridgeCoding NLP:

• https://drive.google.com/file/d/0B_ZOKLUe_XPaNVFHM3M4dHRzV28/view?pli=1
• http://blog.cambridgecoding.com/2016/03/24/misleading-modelling-overfitting-cross-validation-and-the-bias-variance-trade-off/

Annoy:

• Annoy, Luigi: http://erikbern.com/, https://www.hakkalabs.co/articles/approximate-nearest-neighbors-vector-models
• LSH: https://speakerdeck.com/maciejkula/locality-sensitive-hashing-at-lyst
• http://www.slideshare.net/erikbern/approximate-nearest-neighbor-methods-and-vector-models-nyc-ml-meetup
• https://github.com/spotify/annoy

RPForest: https://github.com/lyst/rpforest LightFM: https://github.com/lyst/lightfm Secure because of math: https://www.youtube.com/watch?v=TYVCVzEJhhQ Talking machines: http://www.thetalkingmachines.com/ Dive into DS: https://github.com/rasbt/dive-into-machine-learning

DS process: https://www.oreilly.com/ideas/building-a-high-throughput-data-science-machine Friendship paradox: https://vuhavan.wordpress.com/2016/03/25/ban-ban-ban-nhieu-hon-ban-ban/

AB test:

• notebook: https://github.com/Volodymyrk/stats-testing-in-python/blob/master/01%20-%20Single%20Sample%20tests%20for%20Mean.ipynb
• https://medium.com/@rchang/my-two-year-journey-as-a-data-scientist-at-twitter-f0c13298aee6#.t1h9ouwpg
• http://multithreaded.stitchfix.com/blog/2015/05/26/significant-sample/
• http://nerds.airbnb.com/experiments-at-airbnb/
• https://www.quora.com/When-should-A-B-testing-not-be-trusted-to-make-decisions/answer/Edwin-Chen-1?srid=sL8&share=1

EMNLP 2015:

• semantic sim of embedding: https://www.cs.cmu.edu/~ark/EMNLP-2015/tutorials/34/34_OptionalAttachment.pdf
• social text analysis: https://www.cs.cmu.edu/~ark/EMNLP-2015/tutorials/3/3_OptionalAttachment.pdf
• personality research in NLP: https://www.cs.cmu.edu/~ark/EMNLP-2015/tutorials/2/2_OptionalAttachment.pdf

To read:

• https://github.com/rasbt/algorithms_in_ipython_notebooks
• https://www.blackhat.com/docs/webcast/02192015-secure-because-math.pdf
• http://nirvacana.com/thoughts/becoming-a-data-scientist/
• http://nbviewer.jupyter.org/github/jmsteinw/Notebooks/blob/master/IndeedJobs.ipynb
• http://www.john-foreman.com/data-smart-book.html
• http://www.thetalkingmachines.com/blog/2015/4/23/starting-simple-and-machine-learning-in-meds
• https://github.com/justmarkham/DAT8
• https://github.com/donnemartin/data-science-ipython-notebooks

Idols:

• Alex Pinto: MLSec
• Peadar Coyle: https://peadarcoyle.wordpress.com/, https://github.com/springcoil/pydataamsterdamkeynote, http://slides.com/springcoil/dataproducts-11#/27, https://medium.com/@peadarcoyle/three-things-i-wish-i-knew-earlier-about-machine-learning-54cb0d23ca29#.uc6e049rl
• Radmim: gensim
• Delip Rao: http://deliprao.com/archives/129
• Alex: http://alexanderdyakonov.narod.ru/engcontests.htm
• Yorav: https://www.cs.bgu.ac.il/~yoavg/uni/
• Andreij: http://karpathy.github.io/2015/05/21/rnn-effectiveness/
• Sebastian: http://www.kdnuggets.com/2016/02/conversation-data-scientist-sebastian-raschka-podcast.html
• Joel Grus: http://joelgrus.com/
• Bugra: http://bugra.github.io/

IPython/Jupyter:

• https://docs.google.com/presentation/d/1PHnnkKYgjq1lcSDaVyhZP0Fs7qC70iA07b2Jv0uisUE/mobilepresent?slide=id.g10d199ad72_0_20

LSTM:

• RNN for music: http://erikbern.com/2014/06/28/recurrent-neural-networks-for-collaborative-filtering/
• skflow: https://github.com/tensorflow/skflow/tree/master/examples
• dropout: http://arxiv.org/abs/1409.2329
• seq2seq: http://arxiv.org/abs/1409.3215
• simple char rnn: https://gist.github.com/karpathy/d4dee566867f8291f086
• https://www.tensorflow.org/versions/r0.7/tutorials/recurrent/index.html#the-model
• http://colah.github.io/posts/2015-08-Understanding-LSTMs/

RNN:

• https://www.tensorflow.org/versions/r0.7/tutorials/recurrent/index.html
• Char RNN: http://nbviewer.jupyter.org/gist/yoavg/d76121dfde2618422139
• http://karpathy.github.io/2015/05/21/rnn-effectiveness/
• http://karpathy.github.io/neuralnets/

Unicode:

• ascii fix: http://stackoverflow.com/questions/21129020/how-to-fix-unicodedecodeerror-ascii-codec-cant-decode-byte
• http://nedbatchelder.com/text/unipain/unipain.html#45

EVENTS:

• April 8-10 2016: PyData Madrid
• April 15-17 2016: PyData Florence
• May 6-8 2016: PyData London hosted by Bloomberg
• May 20-21 2016: PyData Berlin
• September 14-16 2016: PyData Carolinas hosted by IBM
• October 7-9 2016: PyData DC hosted by Capital One
• November 28-30 2016: PyData Cologne

Other Conference Dates Coming Soon!

• PyData Chicago
• PyData NYC
• PyData Paris
• PyData Silicon Valley
• pydata amsterdam: http://pydata.org/amsterdam2016/schedule/ https://speakerdeck.com/maciejkula/hybrid-recommender-systems-at-pydata-amsterdam-2016
• gcp 23-24 March
• pycon sg: June 23-25
• emnlp: june, austin, us
• pydata

QUOTES:

• My name is Sherlock Homes. It is my business to know what other people dont know.
• Take the first step in faith. You don't have to see the whole staircase, just take the first step. [M.L.King. Jr]
• "Data data data" he cried impatiently. I can't make bricks without clay. [Arthur Donan Doyle]

STATS:

• http://vietsciences.free.fr/vietnam/bienkhao-binhluan/tuoithovuachuavn.htm

BOOKS:

• http://shop.oreilly.com/product/0636920033400.do
• https://web.stanford.edu/~hastie/StatLearnSparsity_files/SLS_corrected_1.4.16.pdf
• https://leanpub.com/interviewswithdatascientists

CLUSTER:

• distance: http://stackoverflow.com/questions/22433884/python-gensim-how-to-calculate-document-similarity-using-the-lda-model#answer-22756647
• hac with lsi: https://groups.google.com/forum/#!topic/gensim/0Ev8Okf3MCs
• clustering eva: http://scikit-learn.org/stable/modules/clustering.html#clustering-performance-evaluation
• Silhouette analysis: http://scikit-learn.org/stable/auto_examples/cluster/plot_kmeans_silhouette_analysis.html
• https://groups.google.com/forum/#!msg/gensim/ZxauGgh9Vqs/prIMalR8LbgJ
• http://www.site.uottawa.ca/~diana/csi5180/TextClustering.pdf
• http://stackoverflow.com/questions/17537722/better-text-documents-clustering-than-tf-idf-and-cosine-similarity
• http://www.naftaliharris.com/blog/visualizing-dbscan-clustering/
• 30 day indexed: http://googlenewsblog.blogspot.com/2008/05/keeping-good-news-stories-together-just.html
• http://www.mondaynote.com/2013/02/24/google-news-the-secret-sauce/
• http://searchengineland.com/google-news-ranking-stories-30424
• http://nsuworks.nova.edu/cgi/viewcontent.cgi?article=1051&context=gscis_etd
• https://github.com/lmcinnes/hdbscan
• http://nbviewer.jupyter.org/github/lmcinnes/hdbscan/blob/master/notebooks/Benchmarking%20scalability%20of%20clustering%20implementations-v0.6.ipynb
• http://nbviewer.jupyter.org/github/lmcinnes/hdbscan/blob/master/notebooks/Benchmarking%20scalability%20of%20clustering%20implementations%202D%20v0.6.ipynb
• http://nbviewer.jupyter.org/github/lmcinnes/hdbscan/blob/master/notebooks/Comparing%20Clustering%20Algorithms.ipynb

EMBEDDING:

• https://quomodocumque.wordpress.com/2016/01/15/messing-around-with-word2vec/
• http://www.offconvex.org/2016/02/14/word-embeddings-2/
• improving sem embedding words rep: https://levyomer.wordpress.com/2015/03/30/improving-distributional-similarity-with-lessons-learned-from-word-embeddings/
• whiskey: http://wrec.herokuapp.com/methodology
• lda: topic eva: http://radimrehurek.com/topic_modeling_tutorial/2%20-%20Topic%20Modeling.html
• lda2vec: http://www.slideshare.net/ChristopherMoody3/word2vec-lda-and-introducing-a-new-hybrid-algorithm-lda2vec-57135994
• http://nbviewer.jupyter.org/github/cemoody/lda2vec/blob/master/examples/twenty_newsgroups/lda.ipynb
• text2vec: http://dsnotes.com/articles/glove-enwiki
• Swivel Submatrix Wise Vector Embedding Learner http://arxiv.org/pdf/1602.02215v1.pdf
• https://sense2vec.spacy.io/?natural_language_processing%7CNOUN

Linux:

• http://randyzwitch.com/gnu-parallel-medium-data/

BENCHMARK:

• keras vs theano vs tensorflow: https://www.reddit.com/r/MachineLearning/comments/462p41/pros_and_cons_of_keras_vs_lasagne_for_deep/
• http://felixlaumon.github.io/2015/01/08/kaggle-right-whale.html
• https://github.com/zer0n/deepframeworks/blob/master/README.md
• soumith/convnet-benchmarks#66
• https://github.com/soumith/convnet-benchmarks/blob/master/README.md
• https://github.com/lmcinnes/hdbscan/blob/master/notebooks/Benchmarking%20scalability%20of%20clustering%20implementations.ipynb
• https://github.com/szilard/benchm-ml
• word2vec: http://rare-technologies.com/parallelizing-word2vec