BOOKS - Linear Algebra for Data Science, Machine Learning, and Signal Processing
Linear Algebra for Data Science, Machine Learning, and Signal Processing - Jeff Fessler, Raj Rao Nadakuditi 2024 PDF Cambridge University Press BOOKS
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Linear Algebra for Data Science, Machine Learning, and Signal Processing
Author: Jeff Fessler, Raj Rao Nadakuditi
Year: 2024
Pages: 451
Format: PDF
File size: 29.2 MB
Language: ENG



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The book "Linear Algebra for Data Science Machine Learning and Signal Processing" provides an introduction to linear algebra and its applications in data science, machine learning, and signal processing. It covers topics such as vector spaces, linear transformations, eigenvectors, and singular value decomposition, and demonstrates how these concepts can be used to solve real-world problems in data analysis and machine learning. The book also discusses the history and development of linear algebra, highlighting its importance in shaping our understanding of technology and its role in driving innovation and progress. Book Description: Title: Linear Algebra for Data Science Machine Learning and Signal Processing Author: Jeff Fessler, Raj Rao Nadakuditi 2024 451 Cambridge University Press Genre: Non-Fiction, Technology, Mathematics Audience: Students, Researchers, Professionals in Data Science, Machine Learning, and Signal Processing Summary: This book offers a comprehensive introduction to linear algebra and its applications in data science, machine learning, and signal processing.
Книга «Линейная алгебра для машинного обучения науки о данных и обработки сигналов» содержит введение в линейную алгебру и ее приложения в науке о данных, машинном обучении и обработке сигналов. Он охватывает такие темы, как векторные пространства, линейные преобразования, собственные векторы и декомпозиция сингулярных значений, и демонстрирует, как эти понятия могут быть использованы для решения реальных задач в анализе данных и машинном обучении. В книге также обсуждается история и развитие линейной алгебры, подчеркивается ее важность в формировании нашего понимания технологии и ее роли в стимулировании инноваций и прогресса. Линейная алгебра для Data Science Машинное обучение и обработка сигналов Автор: Джефф Фесслер, Радж Рао Надакудити 2024 451 Жанр прессы Кембриджского университета: нон-фикшн, технологии, математика Аудитория: студенты, исследователи, профессионалы в области Data Science, машинное обучение и обработка сигналов Резюме: эта книга предлагает комплексное введение в линейную алгебру и ее приложения в науке о данных, машинном обучении и обработке сигналов.
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