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.
1130642465
Automated Machine Learning: Methods, Systems, Challenges
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.
0.0
In Stock
5
1
Automated Machine Learning: Methods, Systems, Challenges
Automated Machine Learning: Methods, Systems, Challenges
eBook (1st ed. 2019)
FREE
Related collections and offers
0.0
In Stock
Product Details
| ISBN-13: | 9783030053185 |
|---|---|
| Publisher: | Springer-Verlag New York, LLC |
| Publication date: | 05/17/2019 |
| Series: | The Springer Series on Challenges in Machine Learning |
| Sold by: | Barnes & Noble |
| Format: | eBook |
| File size: | 14 MB |
| Note: | This product may take a few minutes to download. |
From the B&N Reads Blog