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Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning
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Automated Machine Learning - Methods, Systems, Challenges (Hardcover, 1st ed. 2019)
Loot Price: R1,304
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Automated Machine Learning - Methods, Systems, Challenges (Hardcover, 1st ed. 2019)
Series: The Springer Series on Challenges in Machine Learning
Expected to ship within 10 - 15 working days
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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.
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