Automated Machine Learning: Methods, Systems, Challenges

Book Description

This open book presents the first comprehensive overview of general methods in Automated (AutoML), collects descriptions of existing based on these methods, and discusses the first series of international challenges of AutoML . 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 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 , based on from optimization and 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.

Book Details

  • Title: Automated Machine Learning: Methods, Systems, Challenges
  • Author: , ,
  • Length: 219 pages
  • Edition: 1st ed. 2019
  • Language: English
  • Publisher:
  • Publication Date: 2019-07-10
  • ISBN-10: 3030053172
  • ISBN-13: 9783030053178
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