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Nº de artículo:
112607187
Linear Algebra for Data Science, Machine Learning, and Signal Processing
86% of respondents would recommend this to a friend
Nº de artículo:
112607187
CRC 43312
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Maximise student engagement and understanding of matrix methods in data-driven applications with this modern teaching package.
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What Stands Out
Comprehensive Coverage
Offers a thorough exploration of linear algebra concepts, tailored specifically for applications in data science, machine learning, and signal processing, ensuring a deep understanding for learners.
Practical Applications
Focuses on real-world applications and problem-solving techniques, enabling readers to effectively apply linear algebra techniques in their projects and research across various emerging fields.
Expertly Crafted
Written by industry experts and academics, this resource provides a blend of theory and practice, equipping readers with the high-quality knowledge necessary to excel in modern analytical environments.
Detalles de producto
- 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 | 16 May 2024 |
| Edition | 1st |
| Language | English |
| Print length | 450 pages |
| ISBN-10 | 1009418149 |
| ISBN-13 | 978-1009418140 |
| Item weight | 930 g |
| Dimensions | 17.15 x 3.18 x 24.13 cm |
Who Should Buy?
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Data Science Students
Students pursuing data science will benefit from foundational linear algebra skills necessary for data analysis and modeling.
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Machine Learning Practitioners
Practitioners looking to deepen their understanding of machine learning algorithms will find valuable insights in this text.
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Signal Processing Engineers
Engineers working in signal processing will appreciate the practical applications of linear algebra in their field.
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Beginners in Mathematics
Complete novices in mathematics may struggle with the complex concepts presented without prior foundational knowledge.
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Telecommunications Editorial Review
Linear Algebra for Data Science, Machine Learning, and Signal Processing offers a comprehensive and essential resource for students and professionals alike. Published by Cambridge University Press, this first edition spans 450 pages, making it a substantial reference on applying linear algebra concepts in practical fields such as machine learning and signal processing. Readers have appreciated its clarity and structured approach, which allows for a smooth learning curve even for complex topics. The book is meticulously designed with a focus on real-world applications, and its engaging writing style enhances the reader's understanding. Its balanced coverage of theory and practice has made it a favorite among learners and educators in data science, illustrating the relevance of linear algebra in modern computing.
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ventajas
- Comprehensive coverage of linear algebra applications
- Easy-to-understand explanations for complex topics
- Includes practical examples from data science
- Well-structured learning progression for readers
- Engaging writing style enhances understanding
Contras
- The print length may feel extensive for some readers
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CRC 43312
Haz tu pedido ahora y recíbelo por ahí Sunday, Octubre 18
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características y beneficios
- Engaging textbook designed for upper-level undergraduates and first-year graduate students.
- Covers both basic and advanced matrix topics relevant to data science and machine learning.
- Includes 200+ multiple-choice questions for interactive classroom learning.
- Features practical exercises with solutions available for instructors.
- Encourages active learning through 'explore' questions and practical Julia code examples.
- Offers a suite of computational notebooks for hands-on learning experiences.
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