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Comprendre l'apprentissage automatique par Shai Shalev-Shwartz, 3ème édition internationale-
30,50 USD
Environ27,31 EUR
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- Achat immédiatComprendre l'apprentissage automatique par Shai Shalev-Shwartz, 3ème édition internationale
Livraison :
3,99 USD (environ 3,57 EUR) Economy Shipping.
Lieu où se trouve l'objet : AVENEL, NJ, États-Unis
Délai de livraison :
Estimé entre le ven. 4 oct. et le mer. 9 oct. à 43230
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Numéro de l'objet eBay :276481388396
Dernière mise à jour le 12 sept. 2024 18:32:54 Paris. Afficher toutes les modificationsAfficher toutes les modifications
Caractéristiques de l'objet
- État
- Contents
- Same as US Edition
- Language:
- English
- International-ISBN
- 9781107512825
- Packaging
- Shrinkwrapped Book - Box Packed
- Features
- International Edition
- Cover-Design
- May Differ from Original Picture
- Shipping
- FAST 3 to 5 Business Day Service on Expedited Opt.
- Product-Type
- INTERNATIONAL PAPERBACK EDITION
- ISBN
- 9781107057135
- Subject Area
- Mathematics, Computers
- Publication Name
- Understanding Machine Learning : from Theory to Algorithms
- Publisher
- Cambridge University Press
- Item Length
- 10.2 in
- Subject
- Algebra / General, Computer Vision & Pattern Recognition
- Publication Year
- 2014
- Type
- Textbook
- Format
- Hardcover
- Language
- English
- Item Height
- 1.1 in
- Item Weight
- 32.2 Oz
- Item Width
- 7.2 in
- Number of Pages
- 410 Pages
À propos de ce produit
Product Identifiers
Publisher
Cambridge University Press
ISBN-10
1107057132
ISBN-13
9781107057135
eBay Product ID (ePID)
171820749
Product Key Features
Number of Pages
410 Pages
Publication Name
Understanding Machine Learning : from Theory to Algorithms
Language
English
Publication Year
2014
Subject
Algebra / General, Computer Vision & Pattern Recognition
Type
Textbook
Subject Area
Mathematics, Computers
Format
Hardcover
Dimensions
Item Height
1.1 in
Item Weight
32.2 Oz
Item Length
10.2 in
Item Width
7.2 in
Additional Product Features
Intended Audience
Scholarly & Professional
LCCN
2014-001779
Dewey Edition
23
Reviews
Advance praise: 'This elegant book covers both rigorous theory and practical methods of machine learning. This makes it a rather unique resource, ideal for all those who want to understand how to find structure in data.' Bernhard Schölkopf, Max Planck Institute for Intelligent Systems, "This elegant book covers both rigorous theory and practical methods of machine learning. This makes it a rather unique resource, ideal for all those who want to understand how to find structure in data." Bernhard Schlkopf, Max Planck Institute for Intelligent Systems
Illustrated
Yes
Dewey Decimal
006.3/1
Table Of Content
1. Introduction; Part I. Foundations: 2. A gentle start; 3. A formal learning model; 4. Learning via uniform convergence; 5. The bias-complexity trade-off; 6. The VC-dimension; 7. Non-uniform learnability; 8. The runtime of learning; Part II. From Theory to Algorithms: 9. Linear predictors; 10. Boosting; 11. Model selection and validation; 12. Convex learning problems; 13. Regularization and stability; 14. Stochastic gradient descent; 15. Support vector machines; 16. Kernel methods; 17. Multiclass, ranking, and complex prediction problems; 18. Decision trees; 19. Nearest neighbor; 20. Neural networks; Part III. Additional Learning Models: 21. Online learning; 22. Clustering; 23. Dimensionality reduction; 24. Generative models; 25. Feature selection and generation; Part IV. Advanced Theory: 26. Rademacher complexities; 27. Covering numbers; 28. Proof of the fundamental theorem of learning theory; 29. Multiclass learnability; 30. Compression bounds; 31. PAC-Bayes; Appendix A. Technical lemmas; Appendix B. Measure concentration; Appendix C. Linear algebra.
Synopsis
Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way. The book provides an extensive theoretical account of the fundamental ideas underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. Following a presentation of the basics of the field, the book covers a wide array of central topics that have not been addressed by previous textbooks. These include a discussion of the computational complexity of learning and the concepts of convexity and stability; important algorithmic paradigms including stochastic gradient descent, neural networks, and structured output learning; and emerging theoretical concepts such as the PAC-Bayes approach and compression-based bounds. Designed for an advanced undergraduate or beginning graduate course, the text makes the fundamentals and algorithms of machine learning accessible to students and non-expert readers in statistics, computer science, mathematics, and engineering., Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way. The book provides a theoretical account of the fundamentals underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. Following a presentation of the basics, the book covers a wide array of central topics unaddressed by previous textbooks. These include a discussion of the computational complexity of learning and the concepts of convexity and stability; important algorithmic paradigms including stochastic gradient descent, neural networks, and structured output learning; and emerging theoretical concepts such as the PAC-Bayes approach and compression-based bounds. Designed for advanced undergraduates or beginning graduates, the text makes the fundamentals and algorithms of machine learning accessible to students and non-expert readers in statistics, computer science, mathematics and engineering., Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. This book explains the principles behind the automated learning approach and the considerations underlying its usage. The authors explain the 'hows' and 'whys' of machine-learning algorithms, making the field accessible to both students and practitioners.
LC Classification Number
Q325.5 .S475 2014
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Évaluations en tant que vendeur (3 475)
- w***t (18)- Évaluations laissées par l'acheteur.6 derniers moisAchat vérifiéItem as described. Item was packed well. Tracking information didn't update, and received the item before tracking showed it had arrived. Didn't need to communicate with seller. I would purchase from this seller again.
- n***0 (222)- Évaluations laissées par l'acheteur.Dernier moisAchat vérifiéBook arrived at the very end of the 13 day delivery window, otherwise all good, adequate packaging, item as described
- 2***a (71)- Évaluations laissées par l'acheteur.Dernier moisAchat vérifiéThe book is a different version than what is described in the listing. On top of that the seller took days to ship the item and did not provide tracking info until requested multiple times. Gave the wrong tracking info to me and now it says my package is sitting in New Jersey awaiting pickup when the company I ordered from is based in India. How does it make sense. I wish I could just get my money back and order from a more reputable seller.
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