國立虎尾科技大學 |

Personalized machine learning

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Personalized machine learning/ Julian McAuley.
作者:	McAuley, Julian.
出版者:	Cambridge, United Kingdom ; New York, NY :Cambridge University Press, : 2022.,
面頁冊數:	x, 326 p. :ill., digital ; : 23 cm.;
附註:	Title from publisher's bibliographic system (viewed on 24 Jan 2022).
標題:	Machine learning. -
電子資源:	https://doi.org/10.1017/9781009003971
ISBN:	9781009003971

Personalized machine learning
McAuley, Julian.

Personalized machine learning[electronic resource] /Julian McAuley. - Cambridge, United Kingdom ; New York, NY :Cambridge University Press,2022. - x, 326 p. :ill., digital ;23 cm.

Title from publisher's bibliographic system (viewed on 24 Jan 2022).

Every day we interact with machine learning systems offering individualized predictions for our entertainment, social connections, purchases, or health. These involve several modalities of data, from sequences of clicks to text, images, and social interactions. This book introduces common principles and methods that underpin the design of personalized predictive models for a variety of settings and modalities. The book begins by revising 'traditional' machine learning models, focusing on adapting them to settings involving user data, then presents techniques based on advanced principles such as matrix factorization, deep learning, and generative modeling, and concludes with a detailed study of the consequences and risks of deploying personalized predictive systems. A series of case studies in domains ranging from e-commerce to health plus hands-on projects and code examples will give readers understanding and experience with large-scale real-world datasets and the ability to design models and systems for a wide range of applications.

ISBN: 9781009003971Subjects--Topical Terms:

561253
Machine learning.

LC Class. No.: Q325.5 / .M386 2022

Dewey Class. No.: 006.31

Personalized machine learning
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520 $a Every day we interact with machine learning systems offering individualized predictions for our entertainment, social connections, purchases, or health. These involve several modalities of data, from sequences of clicks to text, images, and social interactions. This book introduces common principles and methods that underpin the design of personalized predictive models for a variety of settings and modalities. The book begins by revising 'traditional' machine learning models, focusing on adapting them to settings involving user data, then presents techniques based on advanced principles such as matrix factorization, deep learning, and generative modeling, and concludes with a detailed study of the consequences and risks of deploying personalized predictive systems. A series of case studies in domains ranging from e-commerce to health plus hands-on projects and code examples will give readers understanding and experience with large-scale real-world datasets and the ability to design models and systems for a wide range of applications.
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