Shine a spotlight into the Deep Learning "black box". This comprehensive and detailed guide reveals the mathematical and architectural concepts behind Deep Learning models, so you can customize, maintain, and explain them more effectively. Deep Learning can’t be learned by going through a small fragmented set of simplified recipes from which all math has been scrubbed out. This is a mathematical topic and mastery requires understanding the math along with the programming. What is needed is a resource which presents this topic with the requisite amount of math—no more and no less—with the connection between the Deep Learning and math explicitly spelled out. This is exactly what this book strives to provide with its dual presentation of the math and corresponding PyTorch code snippets. Readers need to know Python and the basics of algebra and calculus.