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Item #:
112378884
Linear Algebra for Data Science, Machine Learning, and Signal Processing
87% of respondents would recommend this to a friend
Item #:
112378884
HUF 25336
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What Stands Out
Comprehensive Coverage
Covers essential linear algebra concepts applied to data science and machine learning, providing a solid foundation for students and professionals looking to enhance their analytical skills.
Practical Applications
Focuses on real-world applications in data science, machine learning, and signal processing, helping readers understand how linear algebra is utilized in various industries.
User-Friendly Approach
Designed for both beginners and advanced learners, offering clear explanations and examples that make complex concepts accessible and engaging, ensuring effective learning experiences.
Product Details
- Maximise student engagement and understanding of matrix methods in data-driven applications with this modern teaching package. Students are introduced to matrices in two preliminary chapters, before progressing to advanced topics such as the nuclear norm, proximal operators and convex optimization. Highlighted applications include low-rank approximation, matrix completion, subspace learning, logistic regression for binary classification, robust PCA, dimensionality reduction and Procrustes problems. Extensively classroom-tested, the book includes over 200 multiple-choice questions suitable for in-class interactive learning or quizzes, as well as homework exercises (with solutions available for instructors). It encourages active learning with engaging 'explore' questions, with answers at the back of each chapter, and Julia code examples to demonstrate how the mathematics is actually used in practice. A suite of computational notebooks offers a hands-on learning experience for students. This is a perfect textbook for upper-level undergraduates and first-year graduate students who have taken a prior course in linear algebra basics.
| Publisher | Cambridge University Press |
| Publication date | May 16, 2024 |
| Edition | 1st |
| Language | English |
| Print length | 452 pages |
| ISBN-10 | 1009418149 |
| ISBN-13 | 978-1009418140 |
| Item Weight | 2.05 pounds (930 grams) |
| Dimensions | 6.75 x 1.25 x 9.5 inches (17.1 x 3.2 x 24.1 cm) |
Who Should Buy?
-
Data Science Students
Ideal for students wanting to strengthen their linear algebra skills relevant to data science applications.
-
Machine Learning Practitioners
Helpful for practitioners seeking to understand the mathematical foundations of various machine learning algorithms.
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Signal Processing Engineers
Beneficial for engineers who need a solid grasp of linear algebra to analyze and process signals effectively.
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Complete Beginners
Not suitable for those with no prior experience in mathematics, as concepts may be too advanced.
Product Description
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Linear Editorial Review
Linear Algebra for Data Science, Machine Learning, and Signal Processing is a comprehensive guide that delves into the mathematical foundations essential for understanding complex algorithms in data science and signal processing. Published by Cambridge University Press, it spans 452 pages and features a user-friendly approach for learners. The book's first edition, set to release on May 16, 2024, presents linear algebra concepts in a practical context, making it incredibly relevant for professionals in the tech field. Suitable for readers looking to bolster their machine learning expertise, the book is written in English and offers clear insights into leveraging linear algebra effectively in real-world scenarios.
Customer Reviews & Ratings
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Pros
- Comprehensive coverage of linear algebra concepts
- Practical applications in data science
- User-friendly layout and organization
- Ideal for machine learning enthusiasts
- Relevant examples for signal processing
Cons
- May be challenging for complete beginners
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HUF 25336
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