Picture 1 of 1
Gallery
Picture 1 of 1
Have one to sell?
Understanding Deep Learning - Hardcover, by Prince Simon J.D. - Very Good
US $115.59
or Best Offer
as low as $39.43/mo with
Condition:
- Buy It NowUnderstanding Deep Learning - Hardcover, by Prince Simon J.D. - Very Good
Oops! Looks like we're having trouble connecting to our server.
Refresh your browser window to try again.
Shipping:
US $5.22 USPS Media MailTM.
Located in: Norman, Oklahoma, United States
Delivery:
Estimated between Sat, Nov 8 and Sat, Nov 15 to 94104
Returns:
Seller does not accept returns.
Payments:
.
Special financing available. See terms and apply now- for PayPal Credit, opens in a new window or tab
Earn up to 5x points when you use your eBay Mastercard®. Learn moreabout earning points with eBay Mastercard
Shop with confidence
Seller assumes all responsibility for this listing.
eBay item number:364956226110
Item specifics
- Condition
- Book Title
- Understanding Deep Learning
- ISBN
- 9780262048644
- Subject Area
- Computers
- Publication Name
- Understanding Deep Learning
- Publisher
- MIT Press
- Item Length
- 9.3 in
- Subject
- Intelligence (Ai) & Semantics, Neural Networks
- Publication Year
- 2023
- Type
- Textbook
- Format
- Hardcover
- Language
- English
- Item Height
- 1.5 in
- Item Weight
- 52.6 Oz
- Item Width
- 8.5 in
- Number of Pages
- 544 Pages
About this product
Product Identifiers
Publisher
MIT Press
ISBN-10
0262048647
ISBN-13
9780262048644
eBay Product ID (ePID)
21059341093
Product Key Features
Number of Pages
544 Pages
Language
English
Publication Name
Understanding Deep Learning
Subject
Intelligence (Ai) & Semantics, Neural Networks
Publication Year
2023
Type
Textbook
Subject Area
Computers
Format
Hardcover
Dimensions
Item Height
1.5 in
Item Weight
52.6 Oz
Item Length
9.3 in
Item Width
8.5 in
Additional Product Features
Intended Audience
Trade
LCCN
2023-034369
Dewey Edition
23
Illustrated
Yes
Dewey Decimal
006.31
Table Of Content
Contents Preface xiii Acknowledgements xv 1 Introduction 1 2 Supervised learning 17 3 Shallow neural networks 25 4 Deep neural networks 41 5 Loss functions 56 6 Fitting models 77 7 Gradients and initialization 96 8 Measuring performance 118 9 Regularization 138 10 Convolutional networks 161 11 Residual networks 186 12 Transformers 207 13 Graph neural networks 240 14 Unsupervised learning 268 15 Generative Adversarial Networks 275 16 Normalizing flows 303 17 Variational autoencoders 326 18 Diffusion models 348 19 Reinforcement learning 373 20 Why does deep learning work? 401 21 Deep learning and ethics 420 A Notation 436 B Mathematics 439 C Probability 448 Bibliography 462 Index 513
Synopsis
From machine learning basics to advanced models, this textbook curates the most important ideas and cutting-edge topics to provide a high density of critical information in an intuitive form. Covers current topics such as transformers and diffusion models, Presents challenging concepts in lay terms before dealing them in mathematical form and visual Illustration, Equips readers to implement naive versions of models, Offers a robust suite of instructor resources along with practice problems and programming exercises in Python Notebooks, Suitable for anyone with a basic background in applied mathematics, An authoritative, accessible, and up-to-date treatment of deep learning that strikes a pragmatic middle ground between theory and practice. Deep learning is a fast-moving field with sweeping relevance in today's increasingly digital world. Understanding Deep Learning provides an authoritative, accessible, and up-to-date treatment of the subject, covering all the key topics along with recent advances and cutting-edge concepts. Many deep learning texts are crowded with technical details that obscure fundamentals, but Simon Prince ruthlessly curates only the most important ideas to provide a high density of critical information in an intuitive and digestible form. From machine learning basics to advanced models, each concept is presented in lay terms and then detailed precisely in mathematical form and illustrated visually. The result is a lucid, self-contained textbook suitable for anyone with a basic background in applied mathematics. Up-to-date treatment of deep learning covers cutting-edge topics not found in existing texts, such as transformers and diffusion models Short, focused chapters progress in complexity, easing students into difficult concepts Pragmatic approach straddling theory and practice gives readers the level of detail required to implement naive versions of models Streamlined presentation separates critical ideas from background context and extraneous detail Minimal mathematical prerequisites, extensive illustrations, and practice problems make challenging material widely accessible Programming exercises offered in accompanying Python Notebooks
LC Classification Number
Q325.73.P75 2023
Item description from the seller
Seller feedback (2)
- l***a (303)- Feedback left by buyer.More than a year agoVerified purchaseThanks for the smooth transaction :-)Ll for Puntos de Partida by Ana María Pérez-Gironés and Thalia Dorwick (2020,... (#364956270986)
- n***n (27)- Feedback left by buyer.More than a year agoVerified purchaseGreat price for this item. Shipped promptly and loved the pristine condition.Integrated Chinese 2 Textbook Simplified Chinese by Tao-Chung Yao, Yaohua... (#364956254471)
Product ratings and reviews
Most relevant reviews
- Nov 26, 2024
Awesome
Verified purchase: YesCondition: NewSold by: booksrun
More to explore :
- Simon & Schuster Nonfiction Hardcover Books,
- Nonfiction Learning to Read Hardcover Books,
- Learning to Read Fiction Hardcover Books,
- Simon & Schuster Fiction Hardcover Books,
- J.D. Salinger Hardcover Antiquarian & Collectible Books,
- J.D. Salinger Hardcover Illustrated Nonfiction Books,
- Simon Beckett Hardcover Illustrated Fiction Books,
- J.D. Salinger Hardcover Original Antiquarian & Collectible Books,
- J.D. Salinger Literature Hardcover Antiquarian & Collectible Books,
- National Geographic Learning Staff Fiction Hardcover Books