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Automated Machine Learning

Methods, Systems, Challenges

by Frank Hutter, Lars Kotthoff, Joaquin Vanschoren

Automated Machine Learning

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Book Description

This book presents the first comprehensive overview of general methods in Automated Machine Learning (AutoML), collects descriptions of existing systems based on these methods, and discusses the first series of international challenges of AutoML systems. The recent success of commercial ML applications and the rapid growth of the field has created a high demand for off-the-shelf ML methods that can be used easily and without expert knowledge. However, many of the recent machine learning successes crucially rely on human experts, who manually select appropriate ML architectures (deep learning architectures or more traditional ML workflows) and their hyperparameters. To overcome this problem, the field of AutoML targets a progressive automation of machine learning, based on principles from optimization and machine learning itself. This book serves as a point of entry into this quickly-developing field for researchers and advanced students alike, as well as providing a reference for practitioners aiming to use AutoML in their work.

This open book is licensed under a Creative Commons License (CC BY). You can download Automated Machine Learning ebook for free in PDF format (6.4 MB).

Book Details

Title
Automated Machine Learning
Subject
Computer Science
Publisher
Springer
Published
2019
Pages
223
Edition
1
Language
English
ISBN13
9783030053178
ISBN10
3030053172
ISBN13 Digital
9783030053185
ISBN10 Digital
3030053180
PDF Size
6.4 MB
License
CC BY

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