/machine-learning-materials

This repository shares very helpful materials, available on the Internet, for Machine Learning, Deep Learning, and Reinforcement Learning.

This repository shares very helpful materials, available on the Internet, for Machine and Deep Learning.

Machine Learning:

  • Machine Learning, by Michael Littman, Charles Isbell, and Pushkar Kolhe [Udacity]
  • Machine Learning, by Pedro Domingos [Youtube] (University of Washington)
  • Machine Learning, by Andrew NG [Coursera] (Stanford University + Coursera)
  • Machine Learning, by Yaser Abu-Mostafa [Youtube] (Caltech)
  • Neural Networks, by Hugo Larochelle [Youtube] (UniversitĂ© de Sherbrooke)
  • Neural Networks for Machine Learning, by Geoffrey Hinton [Youtube] (University of Toronto)
  • Machine Learning, by MathematicalMonk [Youtube]

Deep Learning:

  • Introduction to Deep Learning, by Vincent Vanhoucke [Youtube][Udacity]
  • Deep Learning, by Nando de Freitas [Youtube]
  • Convolutional Neural Networks for Visual Recognition (CS231n) (Winter 2016), by Lei-Lei Fi, Andrej Karpathy, and Justin Johnson [Youtube][Stanford]
  • Convolutional Neural Networks for Visual Recognition (CS231n) (Spring 2017), by Lei-Lei Fi, Justin Johnson, and Serena Yeung [Youtube]
  • Natural Language Processing with Deep Learning (CS224n), by Richard Socher [Youtube][Stanford]
  • Intro to Deep Learning with PyTorch, by Luis Serrano, [Udacity]
  • Deep Learning Glossary, Denny Britz, [WILDML]

Reinforcement Learning:

Online Courses:

  • Reinforcement Learning, by David Silver [UCL][Youtube]
  • Deep Reinforcement Learning, by Sergey Levine et al. [UC Berkeley][Youtube]
  • Learning Reinforcement Learning (with Code, Exercises and Solutions), by Denny Britz [WILDML]
  • Reinforcement Learning, by Charles Isbell, Michael Littman, Chris Pryby [Udacity]
  • Reinforcement Learning, Pascal Poupart [UWaterloo]

Talks:

  • Introduction to Reinforcement Learning, Joelle Pineau, McGill University [VideoLectures]

Other Courses:

  • Practical Deep Learning with PyTorch, by Ritchie Ng [Udemy] ($)
  • Introduction to Deep Learning with Neon, by Nervana Team [Youtube]
  • MIT 6.S191 - Introduction to Deep Learning, by Nick Locascio, et al., [Youtube]
  • Introduction to Parallel Computing, by David Luebke, John Owens, Mike Roberts, and Cheng-Han Lee, [Udacity/Youtube]
  • Manning of Massive Datasets, by Jure Leskovec, et al., [Web][Youtube]
  • Machine Learning, Information Retrieval, and Data Analysis, by Victor Lavrenko [Youtube]
  • Data Mining, by Ian Witten [FutureLearn][Youtube]
  • Learn TensorFlow and deep learning, without a Ph.D. [Google Cloud]
  • MIT 6.S191: Deep Reinforcement Learning [Youtube]

Podcasts:

Web:

  • Distill, by Distill [Distill]
  • Colah's Blog, by Chris Olah [GitHub]
  • Seedbank, by Michael Tyka [Seedbank]
  • Deep Learning with Python, by Francois Chollet [GitHub]
  • PyTorch Tutorial [PyTorch]
  • Spinning Up in Deep RL [OpenAI]
  • Practical Deep Learning for Coders [FastAI][Course]
  • A (Long) Peek into Reinforcement Learning [Lilian Weng]
  • Reinforcement Learning [GitHub]

Other Materials:


I will be adding more resources over time.