tuananhktmt / machine-learning-and-computer-vision-references

Repository for Machine Learning and Computer Vision references.

Machine Learning, Deep Learning, and Computer Vision References

Data is the new oil? No: Data is the new soil. ~ David McCandless

⭐ - Recommendations for Beginners.

Awesome Lists

Artificial Intelligence

• ⭐ Awesome Artificial Intelligence - Lightman Wang (General)
• Awesome Artificial Intelligence (AI) - Owain Lewis (General)
• practicalAI - practicalAI
• A list of artificial intelligence tools you can use today - for: 1. Personal use, 2. Business use — Enterprise Intelligence, 2. Business use (cont’d) — Enterprise Functions, and 3. Industry specific businesses
• FirmAI - ML and DS Applications in Industry | ML and DS Applications in Business | ML and DS Applications in Asset Management | ML and DS Applications in Financial

Machine Learning

• ⭐ Machine Learning Mastery - Jason Brownlee (General)
• ⭐ Homemade Machine Learning - Oleksii Trekhleb (Tutorial)
• 2020 Machine Learning Roadmap (Roadmap)
• Python Machine Learning Jupyter Notebooks (Tutorial)
• Machine Learning Mindset (Roadmap)
• Awesome Machine Learning - Joseph Misiti
• 3D Machine Learning - Yuxuan (Tim) Zhang
• Others:
  • mlcourse.ai - Yorko
  • machine learning examples - lazyprogrammer
  • ML From Scratch - eriklindernoren

Deep Learning

• ⭐ Awesome Deep Learning - Christos Christofidis (General)
• ⭐ Deep Learning Papers Reading Roadmap - Flood Sung (Roadmap)
• Awesome Deep Learning Resources - Guillaume Chevalier (General)
• Deep Learning with Python Notebooks (Tutorial)
• Awesome Deep Learning for Video Analysis - Huaizheng (General)
• Awesome 3D Point Cloud Analysis - Yongcheng (Roadmap)
• Edge Detection:
  • ⭐ Awesome Edge Detection Papers - Haoran MO
  • Awesome Background Subtraction - Murari Mandal
  • Awesome Semantic Segmentation - mrgloom
• Object Detection and Tracking:
  • ⭐ Deep Learning Object Detection - Lee hoseong (Roadmap)
  • Deep Learning for Tracking and Detection - Abhineet Singh (Roadmap)
  • Anomaly Detection Resources - Yue Zhao (General)
  • Awesome Anomaly Detection - Lee hoseong (General)
• PyTorch Framework:
  • Awesome Pytorch List - bharathgs (Framework)
  • ⭐ PyTorch Tutorial - Yunjey Choi (Tutorial)
  • PyTorch Beginner - liaoxingyu (Tutorial)

Computer Vision

• Awesome Computer Vision - Jia-Bin Huang (General)
• ⭐ Learn OpenCV - Satya Mallick (Tutorial)

Production

• ⭐ Deep Learning in Production - Amir Hossein Karami (Roadmap)
• PyTorch: ⭐ Model Serving in PyTorch | Deploying PyTorch using Flask and expose a REST API for model inference

Concepts

Mathematics Concepts

• ProofWiki (proofwiki.org): Web
• Book of Proof (Richard Hammack, 2018, 3rd Ed.): Book | Web
• Book of Proofs (bookofproofs.org): Web

Machine Learning Concepts

• ⭐ Pengenalan Pembelajaran Mesin dan Deep Learning (J.W.G. Putra, 2019): Book | GitHub | Web
• Machine Learning Probabilistic Prespective (K.P. Murphy, 2012. The MIT Press): Book | GitHub | Solution | Web
• Pattern Recognition and Machine Learning (C.M. Bishop. 2006. Springer): Book | GitHub | Web
• Mathematics for Machine Learning (M.P. Deisenroth. 2020. Cambridge University Press) Web | Book update. Book printed

Deep Learning Concepts

• Principles of Artificial Neural Networks (Daniel Graupe, 2013): Book
• Principles of Neurocomputing for Science and Engineering (Fredric M. Ham, 2001): Book
• Neural Networks and Deep Learning (M. Nielsen, 2018): Book | GitHub | Web
• ⭐ Neural Networks and Deep Learning (C.C. Aggarwal, 2018. Springer): Book | Web | Slide
• ⭐ Deep Learning (I. Goodfellow, Y. Bengio, & A. Courville. 2016. The MIT Press): Book | GitHub | Web
• Math and Architectures of Deep Learning (K. Chaudhury . 2020. MEAP): Book

Computer Vision Concepts

• ⭐ Computer Vision: Models, Learning, and Inference (Simon J.D. Prince 2012. Cambridge University Pres): Web | Book | GitHub | Matlab Code
• Computer Vision: Algorithms and Application (R. Szeliski 2010. Springer): Book | GitHub | Web

All with Python

Basic Python Books

• CheatSheet > Comprehensive Python Cheatsheet
• ⭐ Python 3 Object-oriented Programming (D. Phillips. 2015. O'Reilly Media): Book | GitHub | Web
• ⭐ Learning Python Design Patterns (G. Zlobin. 2013. Packt): Book | GitHub
• Mastering Python Design Patterns (S. Kasampalis & K. Ayeva. 2018. Packt): Book | GitHub
• ⭐ Clean Code in Python (M. Anaya. 2018. Packt): Book | GitHub

Data Science with Python

• ⭐ Python Data Science Handbook (J. Vanderplas. 2018. O'Reilly Media): Book | GitHub | Web
• ⭐ Python for Data Analysis (W. McKinney. 2018. O'Reilly Media): Book | GitHub | Web
• Python Data Analytics (F. Nelli. 2018. Apress): Book | GitHub
• Data Analysis and Visualization Using Python (O. Embarak. 2018. Apress): Book | GitHub

Machine Learning with Python

• ⭐ Introduction to Machine Learning with Python (A.C. Muler & S. Guido. 2017. O'Reilly Media): Book | GitHub | Web
• Practical Machine Learning with Python (D. Sarkar, R. Bali, and T. Sharma. 2018. Apress): Book | GitHub
• Machine Learning Applications Using Python (P. Mathur. 2019. Apress): Book | GitHub

Deep Learning with Python

• ⭐ Deep Learning with Applications Using Python (N.K. Manaswi, 2018. Apress): Book | GitHub
• ⭐ Dive into Deep Learning - NumPy/MXNet and PyTorch implementations (Aston Zhang, 2020): Book | GitHub
  • Dive into Deep Learning Compiler (Aston Zhang, 2020): Book | GitHub
• ⭐ Deep Learning with PyTorch (Eli Stevens, 2020. MEAP): Book

Computer Vision with Python

• ⭐ Computer Vision with Python 3 (S. Kapur, 2017. Packt): Book | GitHub
• Programming Computer Vision with Python: Tools And Algorithms For Analyzing Images (Jan Erik Solem, 2012. O'Reilly): Book

All with C++

Basic C++ Books

• CheatSheet > C++ Core Guidelines | C++ Cheatsheet | A cheatsheet of modern C++ language and library features | awesome-cpp1 | awesome-cpp2
• cppreference.com > Website
• Matplot++: A C++ Graphics Library for Data Visualization: GitHub
• Programming: Principles and Practice Using C++ (B Stroustrup. 2008. Addison-Wesley Professional): Book
• The C++ Programming Language (B Stroustrup. 2013. Addison-Wesley Professional): Book

Machine Learning with C++

• Hands-On Machine Learning with C++ (K. Kolodiazhnyi, 2020-05. Packt): Book | GitHub

Deep Learning with C++

• C++ Implementation of PyTorch Tutorials for Everyone: GitHub

Image Processing & Computer Vision with C++

• Learning OpenCV 3: Computer Vision in C++ with the OpenCV Library: Book | GitHub
• The CImg Library is a small and open-source C++ toolkit for image processing: Web

Courses

• Neural networks - University De Sherbrooke by Hugo Larochelle (2013): YouTube | Web
• ⭐ Standford Machine Learning - Standford by Andrew Ng (2008): YoutTube
• ⭐ Caltech Machine Learning - Caltech by Yaser Abu-Mostafa (2012-2014): Web
• ⭐ Carnegie Mellon University Deep Learning - CMU by: YouTube | Web
• ⭐ Deeplearning.ai Neural Networks and Deep Learning - Deeplearning.ai by Andrew Ng in YouTube (2010-2014): YouTube
• Standford Neural Networks and Deep Learning - Standford by Fei-Fei Li: YouTube: 2017
• MIT Deep Learning - MIT by Lex Fridman: GitHub | YouTube
• Stanford Deep Learning - Stanford by Andrew Ng: Homepage | Web | Coursera | GitHub
• ⭐ Deep Learning with PyTorch - by sentdex: YouTube
• Computer Vision - Univ. Central Florida by Mubarak Shah YouTube

Deep Learning Frameworks

• Deep Learning with TensorFlow (G. Zaccone & Md.R. Karim, 2018. Packt): Book, Code, and GitHub
• Deep Learning with PyTorch 1.0 (S. Yogesh K, 2019. Packt): Book and Code
• ⭐ Deep Learning with PyTorch (V. Subramanian, 2018. Packt): Book and GitHub
• ⭐ Deep Learning with PyTorch (Eli Stevens, 2020. MEAP): Book, Code
• Deep Learning with Keras (S. Pal & A. Gulli, 2017. Packt): Book and Code
• Project Templates
  • PyTorch: victoresque | moemen95 | L1aoXingyu

Research Groups

Universities

• Standford Univ - Machine Learning Group (Prof. Andrew Ng)
• Standford Univ - Vision and Learning Lab (Prof. Fei-Fei Li)
• Univ of Montreal - Mila (Prof. Yoshua Bengio)
• New York Univ - CILVR Lab (Prof. Yann LeCun)
• Univ of Toronto - Machine Learning (Prof. Geoffrey Hinton)
• Barkeley Univ - Artificial Intelligence Research (BAIR) Lab (Prof. Trevor Darrell)
• MIT - Deep Learning (Lex Fridman)

Corporations

Brain Team - Google AI: TensorFlow, GitHub Google AI Research | Facebook AI: PyTorch, GitHub Facebook Research | Microsoft AI: Microsoft Cognitive Toolkit (CNTK), GitHub Microsoft AI | Amazon AI: Alexa | Apple AI | Alibaba AI: GitHub Alibaba AI | IBM AI | Nvidia AI: GitHub Nvidia AI | Tencent AI: GitHub Tencent AI

Ph.D. in Machine Learning

Machine Learning - Carnegie Mellon University | EECS - University of California — Berkeley | Computer Science - Stanford University | EECS - Massachusetts Institute of Technology | Computer Science - Cornell University

Products

Self-driving Car: Tesla | Waymo | Industrial Autonomy & Robotics: Anki | Mov.ai | AI: Ultralytics LLC | FirmAI | deepdetect.com

AI Start-Up in Indonesia

• ChatBot: kata.ai > github.com/kata-ai & medium.com/kata-engineering | prosa.ai > medium.com/@prosa.ai | bahasa.ai > github.com/bahasa-ai & medium.com/bahasa-ai | aichat.id | konvergen.ai > github.com/konvergen & medium.com/konvergen
• Vision: nodeflux.io > github.com/nodefluxio & medium.com/nodeflux | delligence.ai | grit.id > github.com/grit-id
• Data Analytics: eureka.ai | kepingai.com
• Annotation Service: acquaire - nodeflux.io
• Communities: ai-innovation.id | Indonesia AI Society | atapdata.ai | coleaves.ai | jakartamachinelearning | datascienceID | tau-dataID | aidi.id | idbigdata

Datasets

cvpapers.com | wikipedia.org | datasetlist.com | deeplearning.net | towardsai.net

• MNIST Dataset - New York University by Yann LeCun (1998): Raw
• CIFAR10 Dataset - University of Toronto by Alex Krizhevsky (2009): Raw
• COCO Dataset - COCO Consortium by Tsung-Yi Lin, et. al. (2015): Web | Download (80 classes)
• Open Images dataset - Web
• Multiple Object Tracking (MOT) Benchmark: MOT16 - Univ. of Adelaide by A. Milan, et. al. (2016) | KITTI
• YouTube: YouTube-BoundingBoxes Dataset - E. Real, et. al. | YouTube-8M Dataset - S. Abu-El-Haija, et. al. (2017) | YouTube-VOS Dataset - Ning Xu, et. al. (2018)
• KITTI Dataset - University of Tübingen by Andreas Geiger (2012): Raw | Object 2D | Object 3D | Bird's Eye View (8 classes)
• Boxy Dataset - bosch-ai by Karsten Behrendt (2019): Web | 2D Box | 3D Box | Realtime
• H3D Dataset - Honda by Abhishek Patil et. al. (2019): Paper | Web
• BLVD Dataset - Xian Jiaotong University by Jianru Xue, et. al. (2019): Paper | GitHub
• nuScenes - The nuScenes dataset is a large-scale autonomous driving dataset: Link (23 classes)

Tools

• docker.com: build and ship apps.
• comet.ml: track, compare, explain and optimize experiments and models.
• onnx.ai: open format built to represent machine learning models.
• mlflow.org: an open source platform for the machine learning lifecycle.
• cortex.dev: the open source stack for machine learning engineering.
• Netron: a viewer for neural network, deep learning and machine learning models.
• DIGITS: DIGITS (the Deep Learning GPU Training System) is a webapp for training deep learning models. The currently supported frameworks are: Caffe, Torch, and Tensorflow.

Benchmark

benchmarks.ai | dawn.cs.stanford.edu | mlperf.org | MobilePhone - ai-benchmark.com | GitHub > deep-learning-benchmark - u39kun, DeepBench - baidu-research

• Tools
  • Jetson Nano: Deep Learning Inference Benchmarks
  • 2017-02 - Benchmarking State-of-the-Art Deep Learning Software Tools
  • 2019-07 - Benchmarking TPU, GPU, and CPU Platforms for Deep Learning
  • 2019-04-20 - Google Coral Edge TPU Board Vs NVIDIA Jetson Nano Dev board — Hardware Comparison
• Machine Learning
  • mlperf.org - Fair and useful benchmarks for measuring training and inference performance of ML hardware, software, and services.
• Object Classification
  • MNIST
  • CIFAR-10
  • CIFAR-100
• Object Detection
  • PASCAL VOC
  • COCO
  • KITTI
• Multi-Object Tracking
  • KITTI
• NLP
  • IndoNLU Benchmark

Create Datasets

• Get Image from Sources
  • Creating a dataset using an API with Python
  • How to create a dataset in Google Colab for your Machine Learning projects
  • How to (quickly) build a deep learning image dataset
  • Tips for building large image datasets
• Dataset Tools
  • Dataset Search - google
  • awesome dataset tools - a curated list of awesome dataset tools

Journals, Magazines, and People

Journals

• AI: Artificial Intelligence (Q1) | Journal of Artificial Intelligence Research (Q1) | Artificial Intelligence Review (Q1)
• Machine Learning: Journal of Machine Learning Research (Q1) | Machine Learning (Q1) | Foundations and Trends in Machine Learning (Q1)
• Computer Vision: Image and Vision Computing (Q1) | Computer Vision and Image Understanding (Q1) | International Journal of Computer Vision (Q1)

Magazines: towardsdatascience | paperswithcode | distill | xenonstack | awesomeopensource.com

• AI: towards-artificial-intelligence - AI | towardsdatascience - AI | AI - ID
• Machine Learning: towardsdatascience - ML | ML - ID | jakartamachinelearning
• Deep Learning: paperswithcode - NLP | deeplearningweekly.com
• Computer Vision: paperswithcode - CV

People

• AI: Ayu Purwarianti, Dr (Computer Science, Toyohashi University of Technology) | Igi Ardiyanto, Dr (Robotics, Toyohashi University of Technology) | Muhammad Ghifary, PhD (AI, Victoria University of Wellington)
• Machine Learning: Dwi H. Widyantoro, Dr (Machine Learning, Texas A&M University)

Podcast

• AI: Lex Fridman Podcast | Artificial Intelligence (AI)
• StartUp: Ngobrolin Startup & Teknologi by Imre Nagi