The book website http://aima.cs.berkeley.edu/ has lots of resources.
But it sounds like you are specifically interested in deep learning. A Google researcher wrote a book on deep learning in Python aimed at a general audience - https://www.amazon.com/Deep-Learning-Python-Francois-Chollet... - which might be more directly relevant to your interests.
There's also what I guess you would call "the deep learning book". https://www.amazon.com/Deep-Learning-Adaptive-Computation-Ma...
(People have different preferences for how they like to learn and as you can see I like learning from books.)
(I apologize if you already knew about these things.)
The lobes in the UI are all essentially functions that you double click into to see the graph they use, all the way down to the theory/math.
If you want more comprehensive ways to learn the theory, I highly recommend Stanford's 231n course (http://cs231n.stanford.edu/) and the Goodfellow/Bengio/Courville Deep Learning book (https://www.amazon.com/Deep-Learning-Adaptive-Computation-Ma...)
Seems to have good reviews on Amazon:
Came out in November 2016. Split in 3 parts:
Part I: Applied Math and Machine Learning Basics (Linear Algebra, Probability and Information Theory, Numerical computation)
Part II: Deep Networks: Modern Practices (Deep Feedforward Networks, Regularization, CNNs, RNNs, Practical Methodology & Applications)
Part III: Deep Learning Research (Linear Factor Models, Autoencoders, Representation Learning, Structured Probabilistic Models, Monte Carlo Methods, Inference, Partition Function, Deep Generative Models)
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