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Machine Learning Refined: Foundations, Algorithms, and Applications, Watt, Jerem
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所在地：Gillingham, 英國
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- Book Title
- Machine Learning Refined: Foundations, Algorithms, and Applicatio
- ISBN
- 1108480721
- Subject Area
- Technology & Engineering
- Publication Name
- Machine Learning Refined : Foundations, Algorithms, and Applications
- Item Length
- 10 in
- Publisher
- Cambridge University Press
- Subject
- Signals & Signal Processing
- Publication Year
- 2020
- Type
- Textbook
- Format
- Hardcover
- Language
- English
- Item Height
- 1.1 in
- Features
- Revised
- Item Width
- 7.2 in
- Item Weight
- 48 Oz
- Number of Pages
- 594 Pages
關於產品
Product Information
An intuitive approach to machine learning detailing the key concepts needed to build products and conduct research. Featuring color illustrations, real-world examples, practical coding exercises, and an online package including sample code, data sets, lecture slides, and solutions. It is ideal for graduate courses, reference, and self-study.
Product Identifiers
Publisher
Cambridge University Press
ISBN-10
1108480721
ISBN-13
9781108480727
eBay Product ID (ePID)
21038911704
Product Key Features
Publication Name
Machine Learning Refined : Foundations, Algorithms, and Applications
Format
Hardcover
Language
English
Features
Revised
Subject
Signals & Signal Processing
Publication Year
2020
Type
Textbook
Subject Area
Technology & Engineering
Number of Pages
594 Pages
Dimensions
Item Length
10 in
Item Height
1.1 in
Item Width
7.2 in
Item Weight
48 Oz
Additional Product Features
Edition Number
2
Intended Audience
Scholarly & Professional
Lc Classification Number
Q325.5.W38 2020
Edition Description
Revised Edition
Reviews
'An excellent book that treats the fundamentals of machine learning from basic principles to practical implementation. The book is suitable as a text for senior-level and first-year graduate courses in engineering and computer science. It is well organized and covers basic concepts and algorithms in mathematical optimization methods, linear learning, and nonlinear learning techniques. The book is nicely illustrated in multiple colors and contains numerous examples and coding exercises using Python.' John G. Proakis, University of California, San Diego
Table of Content
1. Introduction to machine learning; Part I. Mathematical Optimization: 2. Zero order optimization techniques; 3. First order methods; 4. Second order optimization techniques; Part II. Linear Learning: 5. Linear regression; 6. Linear two-class classification; 7. Linear multi-class classification; 8. Linear unsupervised learning; 9. Feature engineering and selection; Part III. Nonlinear Learning: 10. Principles of nonlinear feature engineering; 11. Principles of feature learning; 12. Kernel methods; 13. Fully-connected neural networks; 14. Tree-based learners; Part IV. Appendices: Appendix A. Advanced first and second order optimization methods; Appendix B. Derivatives and automatic differentiation; Appendix C. Linear algebra.
Dewey Decimal
006.3/1
Dewey Edition
23
Illustrated
Yes
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