Neural Networks and Deep Learning: A Textbook Second Edition 2023

This is one of the few academic-style books on deep learning, and it focuses on the fundamentals of the subject, including the theory and algorithms that power deep learning. It is a theory/algorithms book and not a programming book. It provides a deep understanding of the mathematics that power DL along with intuitive explanations that make the material accessible. I found the description on backpropagation very enlightening, and was able to connect it to known mathematical principles. A similar book to this one in style is Ian Goodfellow’s book, although the Aggarwal book has more intuition and better explanations. The Aggarwal is also six years more recent. The coverage in the book is extensive, and there are significant updates over the first edition. I like the additions on large language models among others.

Very professional.You can learn basics of the modern theori of AI.

Topics in this book are clearly explained and it is very well organized.

Broad coverage of neural nets history and advanced but not terribly detailed discussion on back propagationCould use more detailed mathematical justification and procedure for reversal of layer hradienti

El libro muestra los conceptos relacionados con Neural Networks y Deep Learning. Es teórico, se requieren buenos conocimientos en cálculo y algebra lineal. No tiene ejercicios prácticos relacionados con librerías o frameworks.

This book was helpful in understanding AI

This is an academic and mathematical textbook on deep learning and is now in its second edition. Each chapter has many exercises to reinforce the concepts introduced. I used the first edition quite extensively as well. This second edition adds a lot of the recent stuff like graph neural networks, transformers, and large language models like GPT. Virtually every major model in deep learning is covered. I like the fact that the author explains equations in plain English. There is lots of intuition, which gives both a rigorous and a feel for the fundamentals of deep learning.

I’m just baffled how a book on deep learning published in 2023 doesn’t discuss embeddings at all. This is such a red flag I stopped reading the rest of the boom.