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Linear Algebra and Learning from Data First Edition by Gilbert Strang ISBN-13: 978-0692196380
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"Linear Algebra and Learning from Data" by Gilbert Strang explores the fundamental concepts of linear algebra and their applications in data science and machine learning. The book emphasizes the importance of linear algebra in understanding and solving problems related to data analysis, including topics such as algorithms, optimization, and dimensionality reduction. Strang presents the material with clarity, incorporating real-world examples and exercises that demonstrate how linear algebra techniques can be utilized in various data-driven fields. Through this integration, the book provides readers with a comprehensive foundation for leveraging linear algebra in the context of modern data learning and analysis.
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Title: Linear Algebra and Learning from Data
Author: Gilbert Strang
Edition: First Edition
ISBN-13: 978-0692196380
Publisher: Wellesley-Cambridge Press
Publication Year: 2019
Pages: 568
Language: English
Topics: Linear Algebra, Data Science, Machine Learning, Mathematics
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"Linear Algebra and Learning from Data" by Gilbert Strang presents a comprehensive exploration of linear algebra concepts with a focus on their applications in data science and machine learning. The book effectively bridges theoretical principles and practical applications, making it accessible for readers with varying backgrounds. Strang's clear explanations, coupled with extensive examples and exercises, allow readers to develop a strong foundational understanding of linear algebra and its relevance in modern data-driven contexts. Overall, this work serves as an invaluable resource for students and professionals seeking to harness the power of linear algebra in their analysis and interpretation of data.
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PDF – ePub: Linear Algebra and Learning from Data First Edition by Gilbert Strang ISBN-13: 978-06921
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