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Can I read Linear Algebra And Optimization With Applications To Machine Learning - Volume II: Fundamentals of Optimization Theory with Applications to Machine Learning 2 on EtoBox?
Linear Algebra And Optimization With Applications To Machine Learning - Volume II: Fundamentals of Optimization Theory with Applications to Machine Learning 2 by Jean H. Gallier, Jocelyn Quaintance is a computer science book available to read on EtoBox.
What is Linear Algebra And Optimization With Applications To Machine Learning - Volume II: Fundamentals of Optimization Theory with Applications to Machine Learning 2 about?
<p>Volume 2 applies the linear algebra concepts presented in Volume 1 to optimization problems which frequently occur throughout machine learning. This book blends theory with practice by not only carefully discussing the mathematical under pinnings of each optimization technique but by applying these techniques to linear programming, support vector machines (SVM), principal component analysis (PCA), and ridge regression. Volume 2 begins by discussing preliminary concepts of optimization theory
Who reads Linear Algebra And Optimization With Applications To Machine Learning - Volume II: Fundamentals of Optimization Theory with Applications to Machine Learning 2?
It is typically read by working professionals who need an authoritative practice reference.
Common subject areas: medicine, law, business, engineering.
- Author
- Jean H. Gallier, Jocelyn Quaintance
- Publisher
- World Scientific, World Scientific Publishing Co. Pte. Ltd.
- Published
- 2020
- Language
- EN
- ISBN
- 9789811206405
- Category
- computer science
- Subjects
- Mathematics, Optimization, Linear
- Updated
- 2026-03-24
Other editions & translations
- Linear Algebra and Optimization with Applications to Machine Learning - Volume I: Linear Algebra for Computer Vision, Robotics, and Machine Learning (2021)
- Linear Algebra And Optimization with Applications to Machine Learning Volume I: Linear Algebra for Computer Vision, Robotics, and Machine Learning (822 Pages) (2020)
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