Artificial intelligence is already ubiquitous in the life sciences, from cancer diagnosis to medical image analysis, from precision agriculture to wildlife monitoring. It is therefore essential for any scientist, especially life scientists, to have a basic understanding of deep learning, the statistical engine behind AI. This book explains the theory behind neural networks and their internal workings in clear terms and with numerous examples. The authors cover the building blocks of neural networks, the mathematical theory, different types of network architectures, the problem of overfitting, and the strategies to avoid it. The most common data types encountered in biological problems are discussed, with suggestions on how to apply deep learning to different cases. Success and failure stories are presented through interviews with leading experts in the field. The book is accompanied by several Python notebooks with practical examples and clearly commented code.