Artigo n.º:
88850221
Deep Learning: Foundations and Concepts
88% of respondents would recommend this to a friend
Artigo n.º:
88850221
AOA 98815
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Os nossos principais Parceiros Logísticos
This essential book equips readers with a robust foundation for potential future specialization in deep learning.
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O que se Destaca
Comprehensive Coverage
This hardcover edition provides an extensive understanding of deep learning, covering foundational principles and advanced concepts, making it suitable for both beginners and seasoned practitioners.
Expert Insights
Authored by leading experts in the field, this book offers valuable insights and real-world applications, helping readers grasp complex topics and stay current with the latest developments in deep learning.
Practical Examples
Includes practical examples and case studies that illustrate theoretical concepts, allowing readers to apply knowledge effectively, enhancing learning and implementation in real-world scenarios.
Detalhes do produto
- 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 | 2 Nov. 2023 |
| Edition | 2024th |
| Language | English |
| Print length | 669 pages |
| ISBN-10 | 3031454677 |
| ISBN-13 | 978-3031454677 |
| Dimensions | 19.69 x 3.81 x 26.67 cm |
Quem Deverá Comprar?
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Students and Researchers
Ideal for students and researchers seeking a comprehensive understanding of deep learning principles and methodologies.
-
Industry Professionals
Beneficial for professionals in tech fields looking to enhance their skills and knowledge in deep learning applications.
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Educators
Great for educators teaching deep learning, providing a strong foundation and detailed concepts for course material.
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Beginners
Not suitable for complete beginners lacking prior knowledge, as it may be too complex without foundational understanding.
DESCRIÇÃO DO PRODUTO
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Perguntas e respostas do cliente
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Pergunta:
Is this book suitable for beginners?
Resposta: Yes, it is designed for newcomers as well as those with experience in machine learning. -
Pergunta:
What subjects does this book cover?
Resposta: It covers central ideas in deep learning and contemporary architectures and techniques. -
Pergunta:
How does the book facilitate learning?
Resposta: It presents complex concepts through textual descriptions, diagrams, mathematical formulae, and pseudo-code.
English edition Christopher M. Bishop Editorial Review
Deep Learning: Foundations And Concepts is a high-quality textbook that effectively teaches the fundamentals of deep learning, including probability theory and recent advancements like Transformers and autoencoders. The book is well-structured and utilizes color effectively to highlight key points, making complex information more digestible. Readers should note that a background in calculus and linear algebra is essential for fully grasping the concepts presented. The book's emphasis on critical elements over excessive detail and the provision of exercise solutions online enhance its utility for self-study. Overall, this textbook is a fantastic reference for anyone serious about exploring deep learning.
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- Well-structured and clearly presented content
- Covers recent advancements in deep learning
- Utilizes color effectively for key points
- Includes online solutions for exercises
- Requires essential math background for understanding
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AOA 98815
Encomende já e receba por volta de Sexta-Feira, Outubro 09
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- Comprehensive introduction to deep learning for both newcomers and experienced individuals.
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- 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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