- MIT Artificial Intelligence
- Fast.ai MOOC
- CMU Deep Learning (youtube)
- UBC ML with Nando de Freitas
- UofT Neural Networks with Geoffrey Hinton
- Coursera DL series with Andrew Ng
- Coursera Advanced DL series
- Coursera & University of Washington ML series
- Coursera PGM with Daphne Koller
- Book: the Deep Learning Book
- Cambridge with David Mackay (youtube)
- Book: Information Theory, Inference and Learning Algorithms by David Mackay
- Stanford CS231n with Fei Fei Li
- UCF
- Georgia Tech & Udacity
- Princeton Robotics
- book: Computer Vision: Algorithms and Applications
- book: Computer Vision: Models, Learning, and Inference
- UC Berkeley Sergey Levine
- UC Berkeley Bootcamp
- UCL David Silver
- Book: RL by Richard Sutton
- Denny Britz github
- MIT Intro to Probability
- MIT Probabilistic Systems Analysis and Applied Probability
- MIT Discreate Stochastic Processes
- Stanford C++ Programming
- book: The C++ Programming Language
- book: Exceptional C++
- book: Python Cookbook
- MIT Intro to Algorithms with Erik Demaine
- MIT Design and Analysis of Algorithms with Erik Demaine
- MIT Advanced Data Structures with Erik Demaine
- MIT Algorithmic Lower Bounds with Erik Demaine
- MIT Geometric Folding Algorithms with Erik Demaine
- Harvard Advanced Algorithms
- Princeton Algorithms part I
- Princeton Algorithms part II
- MIT Programming for the Puzzled
- book: Algorithms Sedgewick
- book: Algorithms CLRS
- CMU Intro to Computer Systems
- Udacity High Performance Computing
- book: Computer Systems: A programmer's perspective
- Udacity Intro to Parallel Programming
- CMU Prallel Computer Architecture and Programming
- book: CUDA by Example