Sản phẩm #:
218689248
Mathematics for Machine Learning
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Sản phẩm #:
218689248
VND 1800117
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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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What Stands Out
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.
Thông tin chi tiết sản phẩm
- 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 | 110845514X |
| ISBN-13 | 978-1108455145 |
| Dimensions | 17.78 x 2.24 x 25.4 cm |
| Part of series | Studies in Natural Language Processing |
Who Should Buy?
-
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.
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Complete Beginners
Not suitable for those with no prior mathematical background, as some concepts may be too advanced to understand.
MÔ TẢ SẢN PHẨM
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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Câu hỏi và trả lời của khách hàng
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câu hỏi:
What are the central machine learning methods discussed in the book?
trả lời: Linear regression, principal component analysis, Gaussian mixture models and support vector machines. -
câu hỏi:
Are there prerequisites needed for understanding the mathematical concepts?
trả lời: No, the book introduces mathematical concepts with a minimum of prerequisites. -
câu hỏi:
Where can programming tutorials be accessed?
trả lời: Programming tutorials are offered on the book's web site.
English edition Marc Peter Deisenroth Format: Paperback Editorial Review
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VND 1800117
Đặt hàng ngay bây giờ và nhận hàng vào khoảng Thứ Bảy, Tháng 10 10
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Các tính năng và lợi ích
- 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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