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Deep Learning: Foundations and Concepts by Christopher M. Bishop
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- Compralo SubitoDeep Learning: Foundations and Concepts by Christopher M. Bishop
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Oggetto che si trova a: Nashville, TN, Stati Uniti
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Consegna prevista tra il mar 5 ago e il lun 11 ago a 94104
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Specifiche dell'oggetto
- Condizione
- Publish Year
- 2023
- Book Title
- Deep Learning: Foundations and Concepts
- ISBN
- 9783031454677
Informazioni su questo prodotto
Product Identifiers
Publisher
Springer International Publishing A&G
ISBN-10
3031454677
ISBN-13
9783031454677
eBay Product ID (ePID)
22062637751
Product Key Features
Number of Pages
Xx, 649 Pages
Publication Name
Deep Learning : Foundations and concepts
Language
English
Publication Year
2023
Subject
Probability & Statistics / General, Intelligence (Ai) & Semantics, General, Databases / General
Type
Textbook
Subject Area
Mathematics, Computers, Science
Format
Hardcover
Dimensions
Item Weight
50.9 Oz
Item Length
10 in
Item Width
7 in
Additional Product Features
Dewey Edition
23
Number of Volumes
1 vol.
Illustrated
Yes
Dewey Decimal
006.31
Table Of Content
Preface.- The Deep Learning Revolution.- Probabilities.- Standard Distributions.- Single-layer Networks: Regression.- Single-layer Networks: Classification.- Deep Neural Networks.- Gradient Descent.- Backpropagation.- Regularization.- Convolutional Networks.- Structured Distributions.- Transformers.- Graph Neural Networks.- Sampling.- Discrete Latent Variables.- Continuous Latent Variables.- Generative Adversarial Networks.- Normalizing Flows.- Autoencoders.- Diffusion Models.- Appendix A Linear Algebra.- Appendix B Calculus of Variations.- Appendix C Lagrange Multipliers.- Biblyography.- Index
Synopsis
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 Hinton " With 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
LC Classification Number
Q334-342
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- n***g (95)- Feedback lasciato dall'acquirente.Ultimi 6 mesiAcquisto verificatoPlease don’t not order from this seller,I ordered the ati book they listed, paid and I was waiting for my order,got a notification that the order has been shipped without a tracking number, I waited 13 days waiting for my order to arrive,contacted the seller that I’m still waiting for my order to arrive,only for the seller to tell me that the item listed and price they put was just for a single book and not the entire set($35) and the seller never contacted me, that mean they sent fake shippingATI Nurse Education Content Mastery Series 10.0 by ATI (N° 296605622308)
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