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]
- Machine Learning, by Andrew NG [Coursera]
- Neural Networks, by Hugo Larochelle [Youtube]
- Neural Networks for Machine Learning, by Geoffrey Hinton [Youtube]
- Introduction to Deep Learning, by Vincent Vanhoucke [Youtube][Udacity]
- Deep Learning, by Nando de Freitas [Youtube]
- Convolutional Neural Networks for Visual Recognition (CS231n), 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]
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]
- Machine Learning, Information Retrieval, and Data Analysis, by Victor Lavrenko [Youtube]
- Data Mining, by Ian Witten [FutureLearn][Youtube]
Podcasts:
- TWiML & AI [SoundCloud]
- Talking Machines [SoundCloud]
- Artificial Intelligence in Industry [SoundCloud]
- Linear Digressions [SoundCloud]
- Element AI [itunes]
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]
Other Materials:
- KDnuggets [KDnuggets]
I will be adding more resources over time.