Deep Learning: Foundations and Concepts
Now Browsing: Textbooks › Artificial Intelligence › Deep Learning: Foundations and Concepts
Hugh Bishop
Published: 2024
Publisher: Springer
Format: Hardcover
Pages: 649
ISBN 13: 9783031454677
ISBN 10: 3031454677
- All
- Rent
- Buy
- eBook
- Sell Back
Bookstore Condition | Item Price Shipping Total Cost Store link |
|---|---|
Also check: Knetbooks, BiggerBooks
Bookstore Condition | Item Price Shipping Total Cost Store link |
|---|---|
Also check: BiggerBooks, Knetbooks, AbeBooks
Bookstore Condition | Item Price Shipping Total Cost Store link |
|---|---|
Bookstore Condition | Item Price Shipping Total Cost Store link |
|---|---|
Bookstore Condition | Item Price Shipping Total Cost Store link |
|---|---|
Also check: Knetbooks, BiggerBooks
Bookstore Condition | Item Price Shipping Total Cost Store link |
|---|---|
Also check: BiggerBooks, Knetbooks, AbeBooks
Bookstore Condition | Item Price Shipping Total Cost Store link |
|---|---|
Prices checked Oct 19, 2026, 5:29 PM ET. Prices change often; the store's checkout price is final.
Renting vs. Buying This Textbook
Renting is usually cheapest if you only need the book for one term: you return it with a free prepaid label when the term ends. Buying used can beat renting when the sell-back price is high, so compare the sell-back price with the rental price before you decide. An eTextbook arrives instantly but usually expires. Rentals and used copies rarely include working access codes.
Always match the ISBN on your syllabus (9783031454677) before you order; different editions have different ISBNs.
About This Book
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…