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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)
Item #: 117993479

Deep Learning (Adaptive Computation and Machine Learning series)

88% of respondents would recommend this to a friend

Item #: 117993479

XCD 219

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“Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.”—Elon Musk
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What Stands Out

Comprehensive Coverage
This book provides an exhaustive overview of deep learning, covering essential algorithms and architectures that empower readers to grasp complex concepts and practical applications effectively.
Adaptive Learning
Utilizing the latest adaptive computation techniques, this edition ensures readers learn at their own pace, making it suitable for both beginners and advanced practitioners in the field.
Practical Examples
Rich with real-world examples and case studies, this resource illustrates theoretical concepts in a hands-on manner, equipping readers with the skills to implement deep learning in various domains.

Product Details

Shop Deep Learning (Adaptive Computation and Machine Learning series) online at a best price in Antigua and Barbuda. B08FH8Y533
Publisher The MIT Press
Publication date November 18, 2016
Language English
Print length 800 pages
ISBN-10 0262035618
ISBN-13 978-0262035613
Item Weight 2.94 pounds (1.33 kg)
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)
Grade level 12 and up
Package Weight3 Pound (1.36 kg)

Who Should Buy?

Suitable For
  • Aspiring Data Scientists

    Individuals seeking foundational knowledge in deep learning to kickstart their careers in data science and analytics.

  • Academic Researchers

    Researchers looking for a comprehensive guide to deep learning concepts for their academic projects and publications.

  • Software Developers

    Developers who want to implement deep learning techniques in applications and improve their machine learning skills.

Not Suitable For
  • Beginner Learners

    Users with no prior knowledge of machine learning may find the content too complex and challenging to follow.

Product Description

Deep Learning (Adaptive Computation and Machine Learning series)

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Customer Questions & Answers

  • Question: Who are the authors of Deep Learning?

    Answer: The book is written by three experts in the field.
  • Question: What topics does the book cover?

    Answer: It covers mathematical foundations, deep learning techniques, and various applications.
  • Question: Is this book suitable for beginners?

    Answer: Yes, it is suitable for undergraduate and graduate students, as well as software engineers.

Intelligence & Semantics Editorial Review

**** The "Deep Learning" book from the Adaptive Computation and Machine Learning series exhibits an adept balance between foundational concepts and cutting-edge research, positioning itself as an essential resource for those serious about the deep learning field. Reviewers commend its extensive coverage of both basic and advanced topics, encapsulating essential elements like linear algebra, optimization, performance metrics, and practical aspects, along with different network architectures such as multi-layer perceptrons and recurrent neural networks. What stands out is the substantial "Deep Learning Research" section that spans over 235 pages, highlighting contemporary research curated by seasoned authors. This selection and cohesive discussion of advanced concepts is highly appreciated, suggesting that the book serves not only as a knowledge resource but also as an integrative summary of current trends in the field. The book is deemed particularly beneficial for readers with a solid grounding in mathematics and machine learning principles. It serves as an advanced follow-up for those who have a foundational understanding of concepts and are looking to explore deeper complexities in neural networks. The insightful observations and mathematical derivations throughout are well-received, enabling an intuitive grasp of advanced ideas and their applications. However, the book is not designed as a programming guide, leading to some misunderstandings among readers expecting it to teach practical coding skills in machine learning systems. Those unfamiliar with the fundamentals of deep learning may find it challenging and perhaps less rewarding. The emphasis on advanced topics means that this book ideally caters to an audience already on the path of specialized study, enhancing their understanding and pushing their capabilities further. Overall, the "Deep Learning" book is well-regarded as an invaluable asset to learners and researchers alike, promising to elevate the reader's comprehension and engagement within the evolving landscape of deep learning. **

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Pros

  • Comprehensive coverage of deep learning fundamentals and advanced topics.
  • In-depth analysis with mathematical derivations and intuitive illustrations.
  • Valuable insights from experienced authors on current research trends.
  • Suitable for individuals with a solid background in linear algebra, calculus, and machine learning.
  • Acts as a robust follow-up resource for readers familiar with basic neural network concepts.

Cons

  • Not intended as a coding or practical implementation guide, which may mislead some readers.

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XCD 219

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Features & Benefits

  • Comprehensive introduction to deep learning concepts and techniques.
  • Covers essential mathematical and conceptual foundations.
  • Explains industry-used deep learning methods and applications.
  • Ideal for students and software engineers entering the field.
  • Includes supplementary material for enhanced learning.
  • Recommended by industry leaders like Elon Musk.

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