Item #:
230596232
Mathematics for Machine Learning
Item #:
230596232
R$ 833
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Product Details
- The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.
| Publisher | Cambridge University Press |
| Publication date | 23 April 2020 |
| Language | English |
| Print length | 390 pages |
| ISBN-10 | 1108470041 |
| ISBN-13 | 978-1108470049 |
| Dimensions | 17.78 x 2.82 x 25.4 cm |
| Part of series | Studies in Natural Language Processing |
Product Description
Mathematics for Machine Learning
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English edition Marc Peter Deisenroth Format: Hardcover Editorial Review
Mathematics For Machine Learning is an insightful resource published by Cambridge University Press on April 23, 2020, featuring 390 pages of essential mathematical concepts geared towards machine learning. It thoroughly covers required topics like vectors, matrices, and linear algebra, making it ideal for practitioners with a foundational understanding of mathematics. Many readers appreciate its practical examples and the well-structured explanations that clarify various mathematical procedures relevant to machine learning. However, potential readers should note that some advanced topics may not align with high school-level math, requiring a certain mathematical maturity to fully benefit from the content.
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Pros
- Excellent explanations of mathematical procedures
- Practical examples enhance understanding
- Covers all essential machine learning topics
- Includes exercises to test knowledge
- Well-structured for easy learning
Cons
- Occasionally lacks deeper explanations for concepts
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