anomic1911 / Learning-path-to-ML-DL

This repo is the path followed by me in my DL/ML journey

Path-to-ML-DL

This repository is roughly based on the path followed by me in my DL/ML journey. It can serve as a rigorous guide for self taught beginners. I have tried to include as many resources as I have used but this list is not exhaustive. For all books you can find them on libgen.io and for videos, you can mostly find them on YouTube.

Linear Algebra

• Gilbert Strang' s lectures Linear Algebra MIT OCW
• Chapter 2 of Deep Learning book (by Ian Goodfellow, short path)
• Schaum's Outline of Linear Algebra book
• Lecture series on Linear Algebra by three blue one brown youtube channel

Probabilty and stochastic processes

• Video Lectures by Prof. S Dharmaraja, IIT Delhi Youtube Link
• Book on Introduction to Probability and Stochastic Processes with Applications by Liliana Blanco Castaneda, Viswanathan Arunachalam, Selvamuthu Dharmaraja, Wiley, Asian Edition, Jan. 2016

Machine Learning

• For videos, refer to Stanford's CS229 Taught by Andrew Ng
• For lecture notes, refer to CS229 course website

Deep Learning

• Hugo Larochelle's Neural networks class (with videos) Neural Networks by Hugo Larochelle
• Stanford's CS230 lecture videos on CS230 course website
• deeplearning.ai's DL specialization on Coursera

Deep Learning for computer vision

• Stanford's CS231n
• Chapter 6, 7, 8, 9 in Deep Learning book

Deep Learning for NLP

• Stanford's CS224n

Reinforcement Learning

• Stanford's CS234 Lecture vidoes
• Berkley's CS294 Course webpage

Must Papers to read on Deep Learning

• Prof. Prathosh's course webpage Google Docs Link