Linear Algebra and Optimization for Machine Learning

Charu C. Aggarwal

18 valoraciones de Goodreads

Idioma: inglés

Editorial: Springer, Springer Sep 2025, 2025

3031986180 / 9783031986185

Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, AlemaniaBuchWeltWeit Ludwig Meier e.K.

Vendedor de 5 estrellas

Vendedor de IberLibro desde 11 de enero de 2012

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Descripción del artículo del vendedor

This item is printed on demand - it takes 3-4 days longer - Neuware -This textbook is the second edition of the linear algebra and optimization book that was published in 2020. The exposition in this edition is greatly simplified as compared to the first edition. The second edition is enhanced with a large number of solved examples and exercises. A frequent challenge faced by beginners in machine learning is the extensive background required in linear algebra and optimization. One problem is that the existing linear algebra and optimization courses are not specific to machine learning; therefore, one would typically have to complete more course material than is necessary to pick up machine learning. Furthermore, certain types of ideas and tricks from optimization and linear algebra recur more frequently in machine learning than other application-centric settings. Therefore, there is significant value in developing a view of linear algebra and optimization that is better suited to the specific perspective of machine learning.It is common for machine learning practitioners to pick up missing bits and pieces of linear algebra and optimization via osmosis while studying the solutions to machine learning applications. However, this type of unsystematic approach is unsatisfying because the primary focus on machine learning gets in the way of learning linear algebra and optimization in a generalizable way across new situations and applications. Therefore, we have inverted the focus in this book, with linear algebra/optimization as the primary topics of interest, and solutions to machine learning problems as the applications of this machinery. In other words, the book goes out of its way to teach linear algebra and optimization with machine learning examples. By using this approach, the book focuses on those aspects of linear algebra and optimization that are more relevant to machine learning, and also teaches the reader how to apply them in the machine learning context. As a side benefit, the reader will pick up knowledge of several fundamental problems in machine learning. At the end of the process, the reader will become familiar with many of the basic linear-algebra- and optimization-centric algorithms in machine learning. Although the book is not intended to provide exhaustive coverage of machine learning, it serves as a technical starter for the key models and optimization methods in machine learning. Even for seasoned practitioners of machine learning, a systematic introduction to fundamental linear algebra and optimization methodologies can be useful in terms of providing a fresh perspective.The chapters of the book are organized as follows.1-Linear algebra and its applications: The chapters focus on the basics of linear algebra together with their common 672 pp. Englisch.…

N° de ref. del artículo 9783031986185

Título
Linear Algebra and Optimization for Machine Learning
Autor
Charu C. Aggarwal
Editorial
Springer, Springer Sep 2025
Año de publicación
2025
Estado
Neu
Encuadernación
Buch
Idioma
inglés
ISBN 10
3031986180
ISBN 13
9783031986185
Edición
2ª Edición
Peso del artículo
1439 gramos
Dimensiones
260x183x42 mm

BuchWeltWeit Ludwig Meier e.K.

Bergisch Gladbach, Alemania

Vendedor de 5 estrellas

Vendedor de IberLibro desde 11 de enero de 2012

Tarifas de envío de Alemania a Estados Unidos de America

ArtículoDe 5 a 15 días hábilesDe 5 a 15 días hábiles
Primer artículoEUR 23,00EUR 23,00
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