Known as the “deep learning bible,” this book provides a thorough introduction to the mathematics, theory, and implementation of neural networks. Topics include linear algebra, probability, optimization, convolutional networks, sequence models, autoencoders, and deep generative models. With rigorous explanations and illustrations, it is a must-read for students, researchers, and professionals who want to master deep learning from the ground up.