Deep Learning: Foundations and Concepts

Deep Learning: Foundations and Concepts by Christopher M. Bishop and Hugh Bishop is a comprehensive and accessible introduction to the world of deep learning. The book effectively balances theoretical depth with practical insights, making it suitable for both beginners and experienced practitioners. Key strengths of the book include: Clear and concise explanations: The authors do an excellent job of breaking down complex concepts into easily understandable terms, making the material accessible to a wide range of readers. Strong mathematical foundation: The book provides a solid mathematical foundation for understanding deep learning algorithms, but it avoids excessive mathematical formalism, making it engaging for readers with varying levels of mathematical background. Practical applications: The book covers a wide range of real-world applications, such as computer vision, natural language processing, and speech recognition, providing practical examples to illustrate the concepts. Up-to-date coverage: The book covers the latest advancements in deep learning, including attention mechanisms, transformer models, and generative adversarial networks. Potential areas for improvement: More hands-on exercises: While the book provides theoretical explanations, it could benefit from more practical exercises and coding examples to reinforce learning. Deeper dives into specific topics: For readers who want to delve deeper into specific topics, such as reinforcement learning or unsupervised learning, additional resources or references could be helpful. Overall, Deep Learning: Foundations and Concepts is an excellent resource for anyone interested in learning about deep learning. It provides a clear and comprehensive introduction to the field, making it a valuable addition to any machine learning enthusiast's library.

I'm feeling lucky. This is incredibly, unspeakably, impossibly good book. I've seen the book at the CVPR conference, and hesitated a bit before buying it. $90 after all. But I can't be happier with my purchase. It's simply the best book on Deep Learning - and on Machine Learning - I've seen in 20 years. It gives a very accessible and intuitive introduction in many topics - and, at the same time, preserves the mathematical rigor. For example, the chapter on transformers is the very, very best description of a topic I've seen so far. The book also contains all the info on the ML basics - from multivariate distributions to EM algorithm. Again, it is written very candidly - and, at the same time, preserves theoretical foundations. It is probably slightly less "mathy" than the "Pattern Recognition and Machine Learning" book by Bishop - which is a previous iteration of this text. But much, much more accessible as well. It explains "narrow" topics in plain English. You may read it in this textbook, and then go back to "Pattern Recognition" if you want to get a slightly deeper math for the same topic. Also the book is very recent and covers the hottest and most recent topics, like Diffusion Models. Saved me lots, lots of time and effort - instead of digging through a heap of research papers I can nos just read a chapter from this book. Many thanks to Chris Bishop for writing it!

Good for beginners and pros. Well written and explained. Up-to-date, but that changes daily, eh? Perfect Gift.

An excellent book on neural networks, deep learning and their applications to AI and computer vision. Extremely well organized with well-connected chapters which makes it easy to follow and get the BIG picture, while diving deep into the specifics using elaborate math language and yet not lacking the intuitive approach. The book can be used either as a one stop-reference for subject matter experts, or simply turned into a self-learning or university level textbook. This book also enjoyed the exceptional and unique style of Prof. Christopher M Bishop , who has been a researcher, a public speaker, and a distinguished leader in the field of NN, DL, ML and AI for decades. Dr. Cazhaow Qazaz VP of Advanced Analytics and AI Knowledge Square LLC

This is quite mathematically rigorous so make sure you're comfortable with your vector calculus and derivations. Otherwise, this is a phenomenal book as it combines it with the mathematical rigor with amazing diagrams, plots and images that demonstrate the intuition behind these common deep learning topics (which is quite rare). For example, he showed a derivation of RMSProp and it's close intuition from physics principles. If you're into research or theory and want to contribute to the field, this is a wonderful entry. If you're in industry and just implementing models that are based on pre-existing theory, you may want to try a more hands-on book.

“This is an excellent book on machine learning and deep learning written by C. Bishop who also wrote a classic book on neural nets in 1995 and a widely used machine learning book in 2006. The book is extremely well-written, with very deep insights on many topics, including recent developments like large language models and graph neural nets. The math derivations are concise and contain insights. Highly recommended for everyone in AI and ML”. Shuiwang Ji Professor and Presidential Impact Fellow Department of Computer Science and Engineering Texas A&M University

I bought this books to acquaint myself on deep learning, but the 2 chapters "Convolutional networks" and "Transformers" are written badly and did not get anything out of this other than a general architecture. How the information is passed and used from one layer to the other is completely missed. Besides those 2 chapters on deep learning, the rest of the book is on topics related to machine learning is easy to follow (if you have some decent background on matrix analysis and statistics).

This book continues the Bishops' tradition of writing accessible and clear yet rigorous treatment of machine learning/deep learning content. The transformers and diffusion chapters fantastic, and exactly what I was looking for. The authors share solutions to end of chapter questions which is perfect for students and self- learners alike. You can scan the book on their site, but this one is a *must have* for your bookshelf. If you need any more reason to purchase this book immediately, LeCun, Bengio and Hinton all wrote highly supportive reviews (see site).

Goodfellow'un 2016 basimli DL kitabi uzerine alanda cok fazla gelisme oldu. DL alaninda teori-matematik bilgileri tazelemek-guncellemek icin iyi bir textbook.

Very nice book. Great explanations, examples and structure

Sehr schönes Buch. Klar und übersichtlich, guter Druck auf gutem Papier. Guter Aufbau des Stoffes. Da ich erst begonnen habe, das Buch durchzuarbeiten, kann ich zu den Details noch nicht all zu viel sagen. Das Schmökern lässt jedoch vermuten, dass dieses Buch die wesentlichen Aspekte des 'Deep Learning' sehr gut zu vermitteln weiss. Das Buch kam aus Indien, Verpackung ungenügend wie immer bei Amazon in der letzten Zeit, daher beschädigte Ecken. Sehr schade! Amazon sollte hinsichtlich Verpackung wirklich eine Anstrengung unternehmen, auch wenn das geringfügig mehr für den Kunden kostet.

Pee chi ama una trattazione matematica rigorosa, è probabilmente tra i migliori