Deep Learning: Foundations and Concepts

Bishopの本は、2006年のPRML以来なので、楽しみにしていました。しかし届いたものは、2ページ乱丁があり、amazonでは初めて返品処理をしました。内容はよいので、ほどなく再度購入したところ、こちらも別の2ページが乱丁でした。なんとか読めるので迷いましたが、やはり返品しました。3度目購入しようかどうか迷っています。海外での1 starレビューを見ると、本書はかなり落丁・乱丁が多いことがわかりました。写真入りのレビューもあります。印刷・製本はきちんとしてほしいです。良書なのに残念です。

事前にGoogle Booksとかで内容を確認してカラー版でグラフも多いと思って購入したのですが、やはり内容が対象浅いと思いました。本当に規則的なことしか扱っていないので、深くこのテーマについて学びたい方は別な参考書を手にした方が良いかもしれません。

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.

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.