Mathematics For Machine Learning
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Product Information
Brand
Pagify
Ideal For
Reading
Material
Paper
Country Of Origin
India
Pack Of
1
Author
Marc Peter Deisenroth, A. Aldo Faisal & Cheng Soon Ong
Genre
Computer Science
Isbn
9781108470049
Page Count
398
Product Type
Book
Product Description
- Premium Quality: Mathematics for Machine Learning is a self-contained textbook that bridges the gap between mathematical and machine learning texts, introducing mathematical concepts with a minimum of prerequisites.
- Product Design: Spanning 398 pages with ISBN 9781108470049, the book is structured to present traditionally disparate mathematical disciplines, including linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability, and statistics, within a single cohesive resource.
- User Experience: The textbook uses mathematical concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models, and support vector machines, offering a rigorous yet approachable learning path.
- Versatile Occasion: Designed for data science and computer science students as well as working professionals who need to efficiently master the mathematical foundations underlying modern machine learning.
- Quality Assurance: For students and others with a mathematical background, the derivations provided offer a principled and thorough understanding of how machine learning methods are constructed.
- Ideal For: Anyone seeking a unified, prerequisite-light introduction to the essential mathematics required to understand and apply machine learning techniques effectively.
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