Mathematics for Machine Learning
This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites
Mathematics for Machine Learning
Item #: 248368981

Mathematics for Machine Learning

Item #: 248368981

THB 6973

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This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites
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Shop Mathematics for Machine Learning online at a best price in Thailand. 1108470041
  • Covers fundamental mathematical tools essential for understanding machine learning
  • Integrates mathematical concepts with machine learning methods like linear regression, principal component analysis, etc.
  • Helps bridge the gap between mathematical and machine learning texts
  • Suitable for data science, computer science students, or professionals
  • Includes worked examples, exercises, and programming tutorials for practical understanding
  • Provides a self-contained approach with minimal prerequisites
Country of OriginThis item will be imported from JP
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Mathematics for Machine Learning

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English Edition Marc Peter Deisenroth , A. Aldo Faisal Editorial Review

Mathematics for Machine Learning, published by Cambridge University Press on April 23, 2020, is an essential read for anyone diving into the intersection of mathematics and machine learning. This well-structured book comprises 390 pages and is designed to tackle complex concepts in a digestible manner. Readers will appreciate the clarity of explanations that cater to individuals with various levels of mathematical backgrounds. The inclusion of helpful examples makes abstract ideas more concrete, thereby enhancing understanding amongst learners. This book's ISBN-13 is 978-1108470049, making it easy to find in various formats.

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Pros

  • Well-structured content for diverse mathematical backgrounds
  • Clear explanations enhance understanding of complex concepts
  • Helpful examples make learning more engaging
  • 390 pages of in-depth exploration
  • Published by a reputable publisher, Cambridge University Press

Cons

  • Some readers may find it dense in certain sections

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