Easy training on custom dataset. Various backends (MobileNet and SqueezeNet) supported. A YOLO demo to detect raccoon run entirely in brower is accessible at https://git.io/vF7vI (not on Windows).

kubernetes

Production-Grade Container Scheduling and Management

livelossplot

Live training loss plot in Jupyter Notebook for Keras, PyTorch and others

lstm_word2vec_spam_classifier

data_science dir: Polish language spam classification using 1) word2vec embeddings with LSTMs, 2) lemmatized vectors with Morfeusz library and Multinomial Naive Bayes algo

mask_rcnn

Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow

mastering-machine-learning-for-penetration-testing

Mastering Machine Learning for Penetration Testing, published by Packt

mit-deep-learning

Tutorials, assignments, and competitions for MIT Deep Learning related courses.

numerical-linear-algebra

Free online textbook of Jupyter notebooks for fast.ai Computational Linear Algebra course

personalized_medicine_cancer_variants_classification

Classification of genetic mutations based on clinical evidence provided as text description

re_ranking_by_optimizing_ndcg_measure

Re-ranking documents optimising NDCG measure with XGBRanker model

skin_cancer_classifier

Benign vs malignant skin cancer classification on material from The International Skin Imaging Collaboration

stanfordnlp

Official Stanford NLP Python Library for Many Human Languages

In this noteboook I will create a complete process for predicting stock price movements. Follow along and we will achieve some pretty good results. For that purpose we will use a Generative Adversarial Network (GAN) with LSTM, a type of Recurrent Neural Network, as generator, and a Convolutional Neural Network, CNN, as a discriminator. We use LSTM for the obvious reason that we are trying to predict time series data. Why we use GAN and specifically CNN as a discriminator? That is a good question: there are special sections on that later.

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