The Little Book of Deep Learning

Comprehensive 2026 MIT Press textbook covering neural networks, CNNs, transformers, GANs, diffusion models, and reinforcement learning with Python notebooks. CC BY-NC-ND licensed.

Simon J.D. Prince

Accessible introduction to neural networks and deep learning explaining backpropagation, convolutional networks, and regularization with interactive examples.

Michael Nielsen

Seminal survey of deep learning history from the inventor of LSTM, covering 800+ references and the evolution of neural network architectures.

Jurgen Schmidhuber

Foundational review of representation learning and deep architectures by Turing Award winner Yoshua Bengio, covering autoencoders and generative models.

Yoshua Bengio, Aaron Courville, and Pascal Vincent