The resources include:
Free machine and deep learning books.
Masters programmes in Artificial Intelligence.
Free online deep learning courses.
Free Machine and Deep Learning Books:
Christoph Rasche, wrote two marvelous 150 pages-long books on Computer Vision and Pattern Recognition, which are available here: http://alpha.imag.pub.ro/~rasche/course/compvis/compvis1.pdf http://alpha.imag.pub.ro/~rasche/course/patrec/patrec1.pdf
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
The classic Elements of Information Theory, by Thoams Cover, available here: http://www.cs-114.org/wp-content/uploads/2015/01/Elements_of_Information_Theory_Elements.pdf
Peltarion’s, Essential Handbook For AI Leaders, is a great little intro to AI for managers.
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.
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/
300 pages of goodies; Intro to Neural Networks Lisbon Machine Learning School 18 June 2018, http://lxmls.it.pt/2018/Lecture.fin.pdf