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Deep Learning: Foundations and Concepts
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Deep Learning: Foundations and Concepts
This essential book equips readers with a robust foundation for potential future specialization in deep learning.
Deep Learning: Foundations and Concepts
Item #: 81984630

Deep Learning: Foundations and Concepts

86% of respondents would recommend this to a friend

Item #: 81984630

DOP 4338

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This essential book equips readers with a robust foundation for potential future specialization in deep learning.
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What Stands Out

Comprehensive Content
Covers comprehensive foundations and advanced concepts in deep learning, making it suitable for both beginners and experts in the field, ensuring a thorough understanding of this rapidly evolving technology.
Up-to-date Edition
The 2024 edition incorporates the latest research and trends in deep learning, providing readers with current information that reflects the state-of-the-art techniques and methodologies in artificial intelligence.
Practical Applications
Focuses on real-world applications and problem-solving strategies, empowering readers to apply deep learning concepts to diverse fields, enhancing their capabilities in both academic and industry settings.

Product Details

Shop Deep Learning: Foundations and Concepts online at a best price in Dominican Republic. 3031454677
  • This book offers a comprehensive introduction to the central ideas that underpin deep learning. It is intended both for newcomers to machine learning and for those already experienced in the field. Covering key concepts relating to contemporary architectures and techniques, this essential book equips readers with a robust foundation for potential future specialization. The field of deep learning is undergoing rapid evolution, and therefore this book focusses on ideas that are likely to endure the test of time.The book is organized into numerous bite-sized chapters, each exploring a distinct topic, and the narrative follows a linear progression, with each chapter building upon content from its predecessors. This structure is well-suited to teaching a two-semester undergraduate or postgraduate machine learning course, while remaining equally relevant to those engaged in active research or in self-study.A full understanding of machine learning requires some mathematical background and so the book includes a self-contained introduction to probability theory. However, the focus of the book is on conveying a clear understanding of ideas, with emphasis on the real-world practical value of techniques rather than on abstract theory. Complex concepts are therefore presented from multiple complementary perspectives including textual descriptions, diagrams, mathematical formulae, and pseudo-code.Chris Bishop is a Technical Fellow at Microsoft and is the Director of Microsoft Research AI4Science. He is a Fellow of Darwin College Cambridge, a Fellow of the Royal Academy of Engineering, and a Fellow of the Royal Society. Hugh Bishop is an Applied Scientist at Wayve, a deep learning autonomous driving company in London, where he designs and trains deep neural networks. He completed his MPhil in Machine Learning and Machine Intelligence at Cambridge University.“Chris Bishop wrote a terrific textbook on neural networks in 1995 and has a deep knowledge of the field and its core ideas. His many years of experience in explaining neural networks have made him extremely skillful at presenting complicated ideas in the simplest possible way and it is a delight to see these skills applied to the revolutionary new developments in the field.” -- Geoffrey HintonWith the recent explosion of deep learning and AI as a research topic, and the quickly growing importance of AI applications, a modern textbook on the topic was badly needed. The New Bishop masterfully fills the gap, covering algorithms for supervised and unsupervised learning, modern deep learning architecture families, as well as how to apply all of this to various application areas. – Yann LeCun“This excellent and very educational book will bring the reader up to date with the main concepts and advances in deep learning with a solid anchoring in probability. Theseconcepts are powering current industrial AI systems and are likely to form the basis of further advances towards artificial general intelligence.” -- Yoshua Bengio
Publisher Springer
Publication date November 2, 2023
Edition 2024th
Language English
Print length 669 pages
ISBN-10 3031454677
ISBN-13 978-3031454677
Item Weight 3.1 pounds (1.41 kg)
Dimensions 7.75 x 1.5 x 10.5 inches (19.7 x 3.8 x 26.7 cm)

Who Should Buy?

Suitable For
  • Students in AI

    Ideal for university students who are studying artificial intelligence and want a solid foundation in deep learning techniques.

  • Industry Professionals

    Great for professionals seeking to enhance their skills in deep learning applications for real-world business problems.

  • Researchers

    Suitable for academic researchers looking for comprehensive theoretical and practical insights into deep learning methodologies.

Not Suitable For
  • Casual Learners

    Not ideal for those looking for a light introductory resource without a rigorous approach to deep learning.

Product Description

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Customer Questions & Answers

  • Question: What topics are covered in Deep Learning: Foundations and Concepts 2024th Edition?

    Answer: This edition explores key topics such as neural networks, supervised and unsupervised learning models, advanced architecture techniques like convolutional and recurrent neural networks, and practical applications in areas like natural language processing and computer vision. By integrating real-world examples and case studies, the book offers a comprehensive overview, making it ideal for both beginners and seasoned practitioners looking to deepen their understanding of deep learning fundamentals.
  • Question: Who is the target audience for this book?

    Answer: The book is targeted towards students, professionals, and enthusiasts in the field of artificial intelligence and machine learning. It's particularly useful for computer science students looking for a solid foundation in deep learning, as well as industry professionals seeking to apply these techniques in practical scenarios. By catering to a diverse audience, the book ensures that readers of varying expertise can grasp complex concepts and apply them effectively.
  • Question: How does this edition differ from previous versions?

    Answer: The 2024th edition offers updated algorithms, enhanced illustrations, and the latest advancements in deep learning. It incorporates feedback from readers of previous editions, ensuring clearer explanations and more relevant examples. This version addresses emerging trends, making it essential for anyone wanting to stay current in the rapidly evolving field of deep learning.
  • Question: Can I use this book for my self-study in deep learning?

    Answer: Absolutely! The structured layout and comprehensive explanations make it ideal for self-study. Each chapter builds on the previous one, allowing readers to progressively develop their knowledge. Additionally, exercises and practical examples provided enhance learning, making it a great resource for independent learners looking to grasp deep learning concepts at their own pace.
  • Question: Does the book include practical examples and projects?

    Answer: Yes, the book features numerous practical examples and projects that illustrate key concepts in action. These examples help bridge the gap between theory and practice, allowing readers to see how deep learning algorithms are applied in real-world situations. Such inclusion empowers readers to replicate projects and experiment with their own implementations, enhancing the learning experience.
  • Question: Is the content suitable for both beginners and advanced users?

    Answer: Yes, this edition is designed to cater to both beginners and advanced users. It starts with fundamental concepts, gradually progressing to more complex topics. This structure allows novices to build a strong foundation while providing advanced practitioners with a comprehensive resource for refining their knowledge and skills in deep learning.
  • Question: What programming languages are covered in this edition?

    Answer: The book primarily focuses on Python, which is widely used in deep learning applications. It also provides insights into libraries and frameworks like TensorFlow and Keras, essential tools for implementing deep learning algorithms. By emphasizing these languages and tools, readers are equipped with the knowledge needed to create practical deep learning models.
  • Question: Are there any supplementary resources or online materials available?

    Answer: Yes, accompanying resources such as code repositories, example scripts, and additional reading materials may be available through the author's website or educational platforms. These supplementary resources enhance the learning experience, providing readers with tangible tools and further information to aid their understanding of deep learning concepts.
  • Question: What advancements in deep learning does this edition focus on?

    Answer: This edition highlights advancements like GANs (Generative Adversarial Networks), transfer learning, and advancements in model optimization. By focusing on these cutting-edge topics, it equips readers with insights into contemporary trends and techniques being adopted in research and industry, making it a vital resource for staying ahead in the field.
  • Question: Where can I buy Deep Learning: Foundations and Concepts 2024th Edition in Dominican Republic?

    Answer: You can purchase Deep Learning: Foundations and Concepts 2024th Edition on Ubuy in Dominican Republic. Ubuy offers a convenient platform for accessing a wide variety of books and educational materials, ensuring that you can obtain the latest editions and resources to enhance your learning in deep learning and artificial intelligence.

Intelligence & Semantics Editorial Review

Deep Learning: Foundations and Concepts is a comprehensive resource authored by Christopher M. Bishop and Hugh Bishop, providing an engaging introduction to deep learning that appeals to both beginners and experts. The book stands out with its clear and concise explanations that break down complex concepts, coupled with a strong mathematical foundation. Readers appreciate its up-to-date coverage on advancements, including large transformers and attention mechanisms. While it succeeds in theory, some readers suggest that more hands-on exercises or coding examples could enhance the learning experience. This balance of rigor and clarity makes it a must-have for anyone serious about diving into machine learning.

Customer Reviews & Ratings

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Pros

  • Clear and concise explanations enhance understanding
  • Strong mathematical foundation for deep learning concepts
  • Covers real-world applications like computer vision
  • Up-to-date on latest advancements in AI
  • Well-organized chapters for easy navigation

Cons

  • Could benefit from more practical exercises and coding examples

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Features & Benefits

  • Over 300,000 available academic books from a leading publisher.
  • Comprehensive introduction to deep learning for both newcomers and experienced individuals.
  • Focus on sustainable practices with print-on-demand publishing.
  • Well-organized bite-sized chapters for easy understanding and teaching.
  • Includes crucial mathematical foundations and practical techniques.
  • Written by experts in the field for clear and effective learning.

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