Automated Machine Learning
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.
Download free tutorial in PDF (220 pages) created by Frank Hutter .
| Pages : | 220 |
| Size : | |
| File type : | HTML |
| Downloads: | 199 |
| Created: | 2021-05-15 |
| License: | Free |
| Author(s): | Frank Hutter |
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Others Machine Learning Tutorials
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An Introduction to Machine Learning, 2nd Edition
Understanding Machine Learning: From Theory to Algorithms
Interpretable Machine Learning: A Guide for Making Black Box Models Explainable
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