This for actual methods: http://www.deeplearningbook.org/
This is also useful, but harder to read than the previous ones: https://web.stanford.edu/~hastie/Papers/ESLII.pdf
Thanks for your suggestion btw.
From the same Stanford publishing there is Introduction to Statistical Learning. It’s a good intro to Machine Learning as a whole.
Far too often it seems people want to jump directly into Deep Learning, I’d shy away from that and having a better understanding of ML as a discipline makes the application of DL much more productive.
Also would like to add a lot of people want to use DL for imaging stuff. Take some time to understand Digital Image Processing as well. It’s a good introduction to convolution and filtering. As well as just understanding what an image is and what can be done with it!
This is just sort of advice from my path.
The second book they mention also had some pretty heavy stuff involving probability and probability models. If you can take some time to understand Automata and it’s supplications such as Hidden Markov Models that’ll be a big help.
Also you mentioning that you never taking a formal algorithm course. While it isn’t necessary as you probably won’t be building anything from scratch. Learning some dynamic programming methods is very helpful when understanding FFT and it’s impact with convolution methods and also how some of these hidden models for probability are evaluated efficiently.