Deep Learning

Ian Goodfellow, Yoshua Bengio, Aaron Courville

The rigorous reference for deep learning theory: the linear algebra, probability, and optimization behind neural networks, then the architectures and training methods.

The foundational graduate textbook of deep learning, covering the mathematical background, classic deep networks, and (as of its writing) research frontiers like generative models.