LINEAR ALGEBRA

• Chapter 2 of Deep Learning book (by Ian Goodfellow, short path) Link

• Lecture series on Linear Algebra by three blue one brown (Highly recommended) Link + singular value decomposition (shortpath).

• And if you have sufficient time, then highly recommended to take “Introduction to Linear Algebra” by Gilbert Strang (on YouTube, long path) Link.

PROBABILITY

• Statistics and probability by Khan academy (short and highly recommended path)Link,

• STATS 110 by Joe Blitzstein, Harvard (long path)Link

Information Theory

• Best way to learn is to do Google search like this ( “what is intuition behind X” ) ( Try to focus on link between KL Divergence and Entropy)

Basic Machine Learning

• Andrew Ng’s coursera

• Chapter 5 (ML basics) Deep Learning book Link

• CS 109 by Harvard Link

• Data science group (IITR) blog posts on basic ML technique.Link

If you don’t have any time constraints then follow step 3 otherwise step 1,2,4.

After completing this do some kaggle problems and get familiar yourself with basic ML implementation. You can start with this awesome by Sebastian Raschka [Link] (https://github.com/rasbt/python-machine-learning-book)

BASIC ML to DL:

• It is very important to understand why basic ML techniques failed on high dimension inputs and about Representation Learning. This is must for your journey in Deep Learning.

• Section 5.11 challenges motivating Deep Learning and chapter 1 from deep learning book

• Representation Learning : A review and new perspectives by Yoshua Bengio (You can leave Probabilistic models and Auto Encoder as of now)

COMPUTER VISION

• Chapter 6, 7, 8, 9 in Deep Learning book Link

• CS231n Convolutional Neural Networks for Visual Recognition (2016 version + generative adversarial networks + deep reinforcement learning from 2017 version)Link

Natural language processing:

• Chapter 1, 2 of Kyunghyun Cho’s lecture notes at NYU (highly recommended - first read this)Link,

• CS224n Deep Learning for NLP Link

Reinforcement Learning :

• It is recommended to first get the idea of convolution neural networks. Later, start with

• Chapter 1 - Sutton’s book on Reinforcement Learning link,

• “Introduction to Reinforcement Learning” by David silver (person behind alphago) Link,