Inside Deep Learning: Math, Algorithms, Models Annotated Edition

I don't know How this author do it, but by far this is the Best DL book in the market for new people in this field. He can mixed perfectly the theory with the practice (a Lot of content in pytorch, loved). He uses colors for explain each part of one equation, that's awesome! It's a indispensable in your library.

This is an excellent manual for modern deep learning: clear explanations for a beginner, while comprehensive enough to serve as a reference for an expert. The practical code examples (especially for how to work with PyTorch packed sequences!) by themselves are valuable enough to justify purchasing a couple dozen copies of this book. For my work, this is the machine learning textbook I most frequently refer to after Murphy’s “Machine Learning: A Probabilistic Perspective.” This book guides the reader through the material that I would consider necessary to know to start a career in either applied deep learning or in machine learning research. I highly recommend this book to both undergraduates and graduate students interested in a serious understanding of deep learning. I wish this book had been available when I was in college, and I am looking forward to the author’s next book.

This is a great addition for any AI practitioner and a very hands on way to get started with neural networks. Highlighted color coding of math equations to demystify terms and functions, breaking it down into understandable nuggets. Code examples in PyTorch that you can copy and paste, while enough detail and explanation to give you a firm understanding of key principles from loss functions to training strategies.

This is a wonderful book. It not only tells you how to use Pytorch and math principles but also shares the author's engineering experience (setting hyperparameters and solutions to certain issues when you may have them in practice).

The book does not provide detailed math as it promises in the title. Some important concepts are either just mentioned or explained in a confusing way. Code organization in some examples is painful to look at. Just bad code. For some examples, code snippets are spread across book sections and are never brought together. It leads to unnecessary confusion.There are better books on this topic.

Great text, useful code, well explained material.

Really good。it explains topics clealy

I think this is best introduction to Deep Learning. It really works best read in a linear fashion from start to finish. It builds a wonderful story arc across the course of the book. Starting from the basics, it builds up concepts gradually, leading eventually to multi-head attention, a very popular and important model.The examples are interesting and well chosen, and are designed to train quickly enough to work with the free Google Colab service (He describes how to set up this service in Appendix A.) The book kept my interest throughout, and I never found myself getting bored or thinking that a certain chapter wasn't that important.It includes the important equations needed to understand how things work. The equations are explained in great detail in words, even adding color coding to match the words to the math. I think it does a great job of teaching the "why" behind techniques.All in all, it was one of the best technical books I've read, and a brilliant way to learn about Deep Learning. It will greatly repay your time to work through it.