Many people (especially students from universities) keep asking me and my coworkers at YerevaNN about good educational resources. They have different levels of math background, some want to study theory and watch visualizations, others want to play with the code before reading formulas..
At some point we understood it's better to spend some time and build a guide that will cover most of these questions (and, as always, we spent a lot more time on this than we expected)
I was looking for something that would answer common questions for some time, but couldn't find anything. Pointing students to research papers unfortunately doesn't work in most of the cases. Deep learning book is too hard for some and they want to see a comparison of different sources.
This guide is designed mostly for those who already know what problems they want to solve, but don't know how to start or where to look for high quality and up-to-date educational resources. Also this is designed for those who want to do research in this area and just want to develop better shovels (that was me 2 years ago).
Regarding datasets, each course we suggest in the guide has its own way of dealing with datasets. Most of them teach how to work with MNIST, which is pretty good for many purposes.
I agree that another guide on datasets could be useful for some people.
At some point we understood it's better to spend some time and build a guide that will cover most of these questions (and, as always, we spent a lot more time on this than we expected)