Best Deep Learning Resources
This is a collection of the best resources for getting into Deep Learning. If you are a novice Deep Learning enthusiast — I am sure it will be very useful to you.
- Khan Academy Linear Algebra videos
- MIT linear algebra videos by Gilbert Strang
- Coding the Matrix — Brown University course on Linear Algebra for CS.
Probability and statistics
General Computer Science
- Algorithms coursera course part 1 and part 2
- MIT course Structure and Interpretation of Computer Programs (based on SICP book)
Artificial Intelligence introduction
- Book “Artificial Intelligence: A Modern Approach (AIMA)”
- Artificial Intelligence course from UC Berkeley(CS 188)
General Machine Learning introduction
- Andrew Ng Machine Learning course on Coursera.
And his course on Deep learning And here’s is the link to the full course Ng taught at Stanford that his coursera course is based upon.
- Pedro Domingos ML course
- Udacity Course on ML by Perer Norvig
- Book “Programming Collective Intelligence”
- TutsPlus course “Machine Learning Distilled”
Deep Learning Basics
- Geoffrey Hinton’s coursera course “Neural Networks for Machine Learning”
- MIT Book on Deep Learning
- UFLDL tutorial by Stanford (alternative link)
- deeplearning.net tutorials
- Metacademy — “package manager” for Machine Learning knowledge
- kaggle — Machine Learning competitions
- mathematicalmonk — Machine Learning youtube tutorials
More DL and ML courses
- NYU Course on Deep Learning
- Anothre course on ML taught at Carnegie Mellon University by Tom Mitchell.
Books about Deep Learning
- Book “Neural Networks and Deep Learning” by Michael Nielsen
- Book Neural “Networks and Learning Machines” by Simon O. Haykin
- deeplearning.net reading list