Numéro d'article:
218689248
Mathematics for Machine Learning
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Numéro d'article:
218689248
VUV 8668
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This self-contained textbook bridges the gap between mathematical and machine learning texts by introducing mathematical concepts with a minimum of prerequisites.
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Ce qui se démarque
Comprehensive Coverage
Covers essential mathematics concepts crucial for machine learning, making it accessible for beginners while providing depth for advanced practitioners, ensuring a well-rounded understanding of the subject.
Practical Applications
Emphasizes real-world applications and examples, helping users to directly relate mathematical concepts to machine learning problems, enhancing retention and practical skill development.
Interactive Learning
Incorporates interactive elements such as exercises and quizzes, fostering an engaging learning environment that promotes active participation and reinforces understanding of complex mathematical ideas.
Détails du produit
| Publisher | Cambridge University Press |
| Publication date | 23 April 2020 |
| Language | English |
| Print length | 390 pages |
| ISBN-10 | 110845514X |
| ISBN-13 | 978-1108455145 |
| Dimensions | 17.78 x 2.24 x 25.4 cm |
| Part of series | Studies in Natural Language Processing |
À qui est-ce destiné ?
-
Aspiring Data Scientists
Ideal for those starting in data science, needing foundational math concepts relevant to machine learning applications.
-
Engineers Transitioning Fields
Beneficial for engineers looking to pivot into machine learning and requiring a solid understanding of mathematical principles.
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Self-Learners
Perfect for self-motivated individuals seeking structured learning resources to grasp the mathematical aspects of machine learning.
-
Complete Beginners
Not suitable for those with no prior mathematical background, as some concepts may be too advanced to understand.
DESCRIPTION DU PRODUIT
Mathematics for Machine Learning
About This Item
Introducing the Mathematics for Machine Learning 1st Edition As the field of machine learning continues to revolutionize various industries, it is essential to have a solid understanding of the mathematical concepts that underpin this powerful technology. The Mathematics for Machine Learning 1st Edition is a comprehensive textbook that covers all the key mathematical foundations needed for successful implementation and application of machine learning algorithms. With endorsements from esteemed experts in the field, such as Joelle Pineau from McGill University and Christopher Bishop from Microsoft Research Cambridge, this book comes highly recommended for both beginners and experienced machine learning researchers and engineers. This self-contained textbook is designed to be accessible to a wide range of readers, with a minimum of prerequisites. It starts with a thorough introduction to linear algebra, which serves as the basis for many machine learning techniques.
From there, it delves into topics such as analytic geometry, matrix decompositions, vector calculus, optimization, probability, and statistics – all of which are crucial for developing a strong understanding of machine learning algorithms. Whether you are a student, a colleague, or simply someone interested in building a solid foundation in machine learning, this book will be an invaluable resource. It presents the necessary mathematical concepts in a clear and concise manner, making it easy to grasp complex ideas and apply them to real-world scenarios. The Mathematics for Machine Learning 1st Edition is not just a tutorial; it is a comprehensive reference text that you can turn to time and time again. It will help you gain a deeper understanding of the mathematical principles behind machine learning algorithms, enabling you to unlock the full potential of this transformative technology. Don't miss out on this essential resource for anyone interested in machine learning.
Order your copy of the Mathematics for Machine Learning 1st Edition today and take your understanding of this exciting field to new heights.
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Questions et réponses des clients
-
question:
What are the central machine learning methods discussed in the book?
répondre: Linear regression, principal component analysis, Gaussian mixture models and support vector machines. -
question:
Are there prerequisites needed for understanding the mathematical concepts?
répondre: No, the book introduces mathematical concepts with a minimum of prerequisites. -
question:
Where can programming tutorials be accessed?
répondre: Programming tutorials are offered on the book's web site.
English edition Marc Peter Deisenroth Format: Paperback Editorial Review
**** "Mathematics for Machine Learning" has garnered Considerable attention from readers, particularly those with an academic background in computer science or related fields. Many customers appreciate the clarity and relevance of the mathematical concepts presented, praising it as one of the best resources for understanding the mathematics crucial for machine learning applications. The book addresses a variety of topics, including vectors, matrices, linear algebra, and probability theory, and presents them in a structured manner that balances theory and practical applications. Readers commend its engaging writing style, which makes complex mathematical ideas more accessible, particularly for those with a foundational understanding of math. The book includes practical examples that relate directly to machine learning, which helps reinforce the theoretical concepts. Additionally, exercises with solutions available on GitHub enhance the learning experience, allowing readers to test their comprehension. However, some feedback suggests that the book may not fully align with its claim of being suitable for "evening learners," as it introduces advanced topics that might require a stronger mathematical foundation than merely high school mathematics. A few readers found the theoretical sections somewhat dense, with a lack of seamless integration between concepts and exercises, which may hinder understanding for some learners. While it is acknowledged that the book provides essential mathematical tools for the development of machine learning skills, clarity and pedagogical coherence were points of contention for several users. Overall, "Mathematics for Machine Learning" is well-received by those looking to deepen their understanding of the mathematical underpinnings of machine learning, though it may require a certain level of mathematical maturity for optimal comprehension. **
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Avantages
- Clear and well-structured presentation of essential mathematical concepts.
- Strong focus on practical applications relevant to machine learning.
- Engaging writing style that makes the material enjoyable to read.
- Includes exercises with solutions to facilitate self-testing and learning.
Les inconvénients
- May be too advanced for individuals without a solid mathematical foundation.
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Caractéristiques et avantages
- Fundamental mathematical tools needed to understand machine learning
- Self-contained textbook for building intuition and practical experience
- Bridges gap between mathematical and machine learning texts
- Introduces mathematical concepts with a minimum of prerequisites
- Chapter exercises and programming tutorials available on the book's website
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