Automated Machine Learning
Automated Machine Learning
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Automated Machine Learning by Frank Hutter
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.“This interesting collection should be useful for AutoML researchers seeking an overview and comprehensive bibliography” (Anoop Malaviya, Computing Reviews, June 14, 2021)
| SKU | Unavailable |
| ISBN 13 | 9783030053178 |
| ISBN 10 | 3030053172 |
| Title | Automated Machine Learning |
| Author | Frank Hutter |
| Series | The Springer Series On Challenges In Machine Learning |
| Condition | Unavailable |
| Binding Type | Hardback |
| Publisher | Springer Nature Switzerland AG |
| Year published | 2019-05-28 |
| Number of pages | 219 |
| Cover note | Book picture is for illustrative purposes only, actual binding, cover or edition may vary. |
| Note | Unavailable |