This repository shares very helpful materials, available on the Internet, for Machine and Deep Learning.
Online Courses:
- 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)
- 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]
Reinforcement Learning:
- 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]
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:
I will be adding more resources over time.