Transfer Learning (Hardcover)

Transfer learning deals with how systems can quickly adapt themselves to new situations, tasks and environments. It gives machine learning systems the ability to leverage auxiliary data and models to help solve target problems when there is only a small amount of data available. This makes such systems more reliable and robust, keeping the machine learning model faced with unforeseeable changes from deviating too much from expected performance. At an enterprise level, transfer learning allows knowledge to be reused so experience gained once can be repeatedly applied to the real world. For example, a pre-trained model that takes account of user privacy can be downloaded and adapted at the edge of a computer network. This self-contained, comprehensive reference text describes the standard algorithms and demonstrates how these are used in different transfer learning paradigms. It offers a solid grounding for newcomers as well as new insights for seasoned researchers and developers.

General

Imprint:	Cambridge UniversityPress
Country of origin:	United Kingdom
Release date:	February 2020
Authors:	Qiang Yang • Yu Zhang • Wenyuan Dai • Sinno Jialin Pan
Dimensions:	235 x 156 x 21mm (L x W x T)
Format:	Hardcover
Pages:	390
ISBN-13:	978-1-107-01690-3
Categories:	Books > Science & Mathematics > Mathematics > Probability & statistics Books > Computing & IT > General theory of computing > Mathematical theory of computation Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning Books > Computing & IT > Applications of computing > Artificial intelligence > Natural language & machine translation Books > Computing & IT > Applications of computing > Artificial intelligence > Computer vision
LSN:	1-107-01690-8
Barcode:	9781107016903

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