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] (Université 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:

• TWiML & AI [SoundCloud]
• Talking Machines [SoundCloud]
• Artificial Intelligence in Industry [SoundCloud]
• Linear Digressions [SoundCloud]
• Element AI [itunes]
• DataFramed [SoundCloud]

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:

• KDnuggets [KDnuggets]
• DataCamp [DataCamp]

^{I will be adding more resources over time.}