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This is a tiny and very focused collection of links about deep learning. If you've always wanted to learn deep learning stuff but don't know where to start, you might have stumbled upon the right place!

• Deep Learning Foundations
• Deep Computer Vision
• Deep Sequence Models
• Deep Generative Models
• Deep Reinforcement Learning
• Deeper: What's next?

This self-learning plan is split into five modules and designed to be completed in five weekends. Per module, you might want to take about four hours to digest the theory plus an additional six or more hours to experiment with available code.

1. Familiarity with basic multivariate calculus, linear algebra, probability, statistics
   • 2018-Mar Niklas Donges: Basic Linear Algebra for Deep Learning
   • 2018-Jul Terence Parr et al: The Matrix Calculus You Need for Deep Learning
   • 2018-Dec Laxman Vija: Probability and Stats in the context of Deep Learning
2. Coding experience with Python or any other programming language
   • SoloLearn: Python | Jay Alammar: NumPy Intro | Stanford: NumPy Intro
3. (Recommended) S Keshav: How to Read a Paper

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