Deep Learning Adaptive Computation and Machine Learning series
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Deep Learning Adaptive Computation and Machine Learning series
“Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” — Elon Musk
Deep Learning Adaptive Computation and Machine Learning series
Artikelnr.: 113167464

Deep Learning Adaptive Computation and Machine Learning series

Artikelnr.: 113167464

€ 123

€ 131

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Shop Deep Learning Adaptive Computation and Machine Learning series online at a best price in Austria. 0262035618
  • Comprehensive introduction to deep learning
  • Covers mathematical and conceptual background
  • Describes deep learning techniques used in industry
  • Surveys applications in natural language processing, computer vision, and more
  • Offers research perspectives on theoretical topics in deep learning
  • Suitable for undergraduate and graduate students, as well as software engineers
Publisher The MIT Press
Publication date November 18, 2016
Language English
Print length 800 pages
ISBN-10 0262035618
ISBN-13 978-0262035613
Item Weight 1.33 Kilograms
Reading age 18 years and up
Dimensions 9.1 x 7.2 x 1.1 inches (23.1 x 18.3 x 2.8 cm)

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Deep Learning Adaptive Computation and Machine Learning series

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Kundenfragen und -antworten

  • Frage: Who authored the book?

    Antworten: The book is written by three experts in the field of deep learning.
  • Frage: What topics does the book cover?

    Antworten: It covers mathematical concepts, deep learning techniques, and practical applications across various industries.
  • Frage: Is this book suitable for beginners?

    Antworten: Yes, it is suitable for undergraduate and graduate students, as well as software engineers new to deep learning.

English Edition Ian Goodfellow , Yoshua Bengio Human Vision & Language Systems Editorial Review

The "Deep Learning" text from the Adaptive Computation and Machine Learning series is a highly regarded introductory book authored by prominent researchers, making it an essential resource for anyone delving into the field of deep learning. The work's popularity can largely be attributed to its engaging and accessible approach, which caters to both students and professionals new to machine learning. The author’s clear explanations, particularly in the introductory mathematics and fundamental concepts of deep learning, are noted for their effectiveness in supporting learners at various levels. Despite its substantial length—nearly 800 pages—many readers appreciate the book's depth and clarity in explaining intricate topics such as Generative Adversarial Networks (GANs). Some users highlighted the valuable information on GANs that is hard to find in Japanese-language resources, affirming the book's role in bridging knowledge gaps. The book is well structured, dividing content into basic knowledge, supervised learning, and unsupervised learning, helping readers navigate through complex material. Nevertheless, some criticisms have surfaced regarding the outdated information compared to the rapidly evolving field of deep learning. While it serves as a solid foundation, users have indicated that supplementing it with more current literature, especially texts that include practical code examples, is advisable for practitioners seeking hands-on experience. Furthermore, readers noted that the physical format may pose challenges for on-the-go studying due to its size. Overall, the book stands out as a definitive guide in deep learning, lauded for its comprehensive yet comprehensible treatment of core concepts, though it may necessitate additional resources for those looking to stay updated on the latest advancements. **

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Vorteile

  • Written by renowned researchers making it an authoritative source
  • Clear, detailed explanations suitable for beginners and those unfamiliar with machine learning
  • Valuable content on GANs not found in other translations
  • Well-structured: separates basic knowledge from advanced topics

Nachteile

  • Length and weight make it impractical for mobile use

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€ 123

€ 131

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Merkmale und Vorteile

  • Comprehensive introduction to deep learning.
  • Covers mathematical and conceptual foundations.
  • Includes industry techniques like convolutional networks.
  • Surveys applications in NLP, speech recognition, and more.
  • Suitable for undergraduate, graduate, and software engineers.
  • Supplementary materials available online.

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