Automated Machine Learning: Methods, Systems, Challenges


Automated Machine Learning: Methods, Systems, Challenges (The Springer Series on Challenges in Machine Learning)
ISBN-10 书号: 3030053172
ISBN-13 书号: 9783030053178
Edition 版本: 1st ed. 2019
Release Finelybook 出版日期: 2019-05-18
Pages 页数: (219 )

The Book Description robot was collected from Amazon and arranged by Finelybook
This open access 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.


Part l.AutoML Methods
1.Hyperparameter Optimization
2.Meta-Learning
3.Neural Architecture Search
Part ll.AutoML Systems
4.Auto-WEKA:Automatic Model Selection and Hyperparameter Optimization in WEKA
5.Hyperopt-Sklearn
6.Auto-sklearn:Efficient and Robust Automated Machine Learning
7.Towards Automatically-Tuned Deep Neural Networks
8.TPOT:A Tree-Based Pipeline Optimization Tool for Automating Machine Learning
9.The Automatic Statistician
Part ll.AutoML Challenges
10.Analysis of the AutoML Challenge Series 2015-2018

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