The resources include:

  1. Free machine and deep learning books.

  2. M.Sc /Ph.D programmes in Artificial Intelligence.

  3. Free online deep learning courses.

Free Machine and Deep Learning Books:

  • Written by Alex Smola, Dive into Deep Learning, An interactive deep learning book with code, math, and discussions is a free 649 pages-long PDF which is available here: http://d2l.ai/

machine learning books
free deep learning books
  • A free, 552 pages-long PDF open source, differential calculus textbook aimed at standard first year university Calculus 1 courses which is available here: http://www.math.ubc.ca/~CLP/index.html  

free machine learning books
free deep learning books
free deep learning books
deep learning interview questions and answers
 
deep learning interview questions and answers

Probability and Statistics - The Science of Uncertainty, Second Edition, is now available for free online.


deep learning interview questions book

The authors have made the book "Mathematics for Machine Learning" publicly available as a PDF even before the official publication date. Its is available https://mml-book.github.io/


deep learning interview questions and answers

Masters degree programs in Artificial Intelligence and Deep Learning:


Recommended Deep Learning courses:

I have been asked many times what is the best way to learn PyTorch. Except for writing Kaggle Kernels 24 hours a day and attending my meetups ... IMHO this is the best online resource for learning PyTorch. Two different courses are available, one for v0.3 and one for v0.4. Highly recommended. 
https://fleuret.org/ee559/
https://documents.epfl.ch/users/f/fl/fleuret/www/dlc/

  1. CS 598 LAZ: Cutting-Edge Trends in Deep Learning and Recognition, stands out as one of the best I have seen so far: http://slazebni.cs.illinois.edu/spring17/

  2. Basic ML algorithms and deep neural networks with PyTorch https://goku.me/practicalAI

  3. PyTorch course at FastCampus and on Github

  4. 300 pages of goodies; Intro to Neural Networks Lisbon Machine Learning School 18 June 2018, http://lxmls.it.pt/2018/Lecture.fin.pdf