Neural Networks and 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

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

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