Unsupervised Feature Learning and Deep Learning - A Review and New Perspectives

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

Compact yet thorough overview of deep learning architectures, training techniques, and modern models designed to fit in a pocket.

François Fleuret

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

Jurgen Schmidhuber