Item #:
230596232
Mathematics for Machine Learning
Item #:
230596232
BHD 52
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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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Product Details
- 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
| 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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Features & Benefits
- Textbook covers the fundamental mathematical tools required for understanding machine learning
- Introduces mathematical concepts with minimum prerequisites
- Uses concepts to derive four central machine learning methods
- Includes worked examples and exercises in every chapter
- Programming tutorials offered on book's website
- Suitable for students with mathematical background or those learning the mathematics for the first time
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