Hillclimbing & Sprinting – The Essential Manual
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Page : pages
File Size : 29,64 MB
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ISBN : 9781845845063
Author :
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Page : pages
File Size : 29,64 MB
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ISBN : 9781845845063
Author : Phil Short & Steve Wilkinson
Publisher : David and Charles
Page : 195 pages
File Size : 15,5 MB
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ISBN : 1845845064
Hillclimbing & Sprinting is one of the most popular ways of getting into UK motorsport and over 400 such events take place each year. This book shows competitors how to get started, how to improve and how to drive the top courses, with extensive information on this sport.
Author : Institution of Engineers (India)
Publisher :
Page : 462 pages
File Size : 20,28 MB
Release : 1971
Category : Engineering
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Page : 462 pages
File Size : 12,52 MB
Release : 1971
Category : Mechanical engineering
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Page : 1302 pages
File Size : 45,4 MB
Release : 1984
Category : Automobile racing
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Page : 250 pages
File Size : 15,73 MB
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Category : Engineering
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Page : 844 pages
File Size : 36,93 MB
Release : 1963
Category : Chemical engineering
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Page : 830 pages
File Size : 22,24 MB
Release : 1963
Category : Chemical engineering
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Page : 506 pages
File Size : 21,36 MB
Release : 1968
Category : Electric railroads
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Author : Frank Hutter
Publisher : Springer
Page : 223 pages
File Size : 38,97 MB
Release : 2019-05-17
Category : Computers
ISBN : 3030053180
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.