The full text of Simon J.D. Prince's book Understanding Deep Learning is available online for free -- though the author asks that you buy the book and write a (positive, one would hope) review on Amazon. He will make a 2nd edition if sales are good.

The book is around 500 pages and a glance at the table of contents shows it goes from fundamentals to very advanced topics: Supervised learning, shallow neural networks, deep neural networks, loss functions (maximum likelihood, univariate regression, classification, cross-entropy, etc), gradient descent, stochastic gradient descent, initialization, the backpropagation algorithm, hyperparameters, regularization, convolutional neural networks, residual networks, transformers, graph neural networks, unsupervised learning, generative adversarial networks (styleGAN, etc), normalizing flows, variational autoencoders, diffusion models, reinforcement learning, why does deep learning work? and ethics. Appendices for notation, mathematics, and probability.

Simon J.D. Prince: Understanding Deep Learning

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