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Deep Learning Generalization
US $64.99
ApproximatelyS$ 84.29
Condition:
Brand New
A new, unread, unused book in perfect condition with no missing or damaged pages.
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Located in: Lansdale, PA, United States
Delivery:
Estimated between Mon, 27 Oct and Mon, 3 Nov to 94104
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eBay item number:357596609189
Item specifics
- Condition
- Brand New: A new, unread, unused book in perfect condition with no missing or damaged pages. See all condition definitionsopens in a new window or tab
- ISBN
- 9781032841892
About this product
Product Identifiers
Publisher
CRC Press LLC
ISBN-10
1032841893
ISBN-13
9781032841892
eBay Product ID (ePID)
19074796388
Product Key Features
Number of Pages
200 Pages
Publication Name
Deep Learning Generalization : Theoretical Foundations and Practical Strategies
Language
English
Publication Year
2025
Subject
Complex Analysis
Type
Textbook
Subject Area
Mathematics
Format
Trade Paperback
Dimensions
Item Length
9.2 in
Item Width
6.1 in
Additional Product Features
Intended Audience
Trade
Dewey Edition
23
Illustrated
Yes
Dewey Decimal
006.31
Synopsis
This book provides a comprehensive exploration of generalization in deep learning, focusing on both theoretical foundations and practical strategies. It delves deeply into how machine learning models, particularly deep neural networks, achieve robust performance on unseen data. Key topics include balancing model complexity, addressing overfitting and underfitting, and understanding modern phenomena such as the double descent curve and implicit regularization. The book offers a holistic perspective by addressing the four critical components of model training: data, model architecture, objective functions, and optimization processes. It combines mathematical rigor with hands-on guidance, introducing practical implementation techniques using PyTorch to bridge the gap between theory and real-world applications. For instance, the book highlights how regularized deep learning models not only achieve better predictive performance but also assume a more compact and efficient parameter space. Structured to accommodate a progressive learning curve, the content spans foundational concepts like statistical learning theory to advanced topics like Neural Tangent Kernels and overparameterization paradoxes. By synthesizing classical and modern views of generalization, the book equips readers to develop a nuanced understanding of key concepts while mastering practical applications. For academics, the book serves as a definitive resource to solidify theoretical knowledge and explore cutting-edge research directions. For industry professionals, it provides actionable insights to enhance model performance systematically. Whether you're a beginner seeking foundational understanding or a practitioner exploring advanced methodologies, this book offers an indispensable guide to achieving robust generalization in deep learning., This book provides a comprehensive exploration of generalization in deep learning, focusing on both theoretical foundations and practical strategies. It delves deeply into how machine learning models, particularly deep neural networks, achieve robust performance on unseen data.
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Popular categories from this store
Seller feedback (73)
- 2***y (837)- Feedback left by buyer.Past monthVerified purchaseGreat Seller, Easy to work with Great item
- Automatische feedback van eBay- Feedback left by buyer.Past monthBestelling op tijd geleverd zonder problemen
- Automatische feedback van eBay- Feedback left by buyer.Past monthBestelling op tijd geleverd zonder problemen