Linear Algebra and Optimization for Machine Learning

Name: Linear Algebra and Optimization for Machine Learning
Brand: Springer, Berlin
SKU: 9783031986185
Price: 20819 HUF
Availability: InStock

A Textbook

Szerző: Charu Aggarwal

Nyelv:

Kötés: Kemény kötésű

Kiadó: Springer, Berlin

Elérhetőség: Beszállítói készleten

Küldés 10-13 napon belül

20 819 Ft

This textbook is the second edition of the linear algebra and optimization book that was published i...

Információk a könyvről

Szerző

Charu Aggarwal

Nyelv

Angol

Kötés

Könyv - Kemény kötésű

Kiadva

2025

oldal

650

EAN

9783031986185

Enbook ID

48984776

Kiadó

Springer, Berlin

Súly

1439

Méretek

178 x 254

Teljes leírás

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.

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Linear Algebra and Optimization for Machine Learning

A Textbook

Információk a könyvről

Teljes leírás

Érdekelheti

Azok a vásárlók, akik ezt a könyvet megvásárolták, a következőket is megvásárolták