Transfer Learning


Transfer Learning
Authors: Qiang Yang
Year: 2020
Publisher: Cambridge University Press
Language: English
ISBN 13: 9781139061773
ISBN 10: 1139061771
Categories: Computers, Artificial Intelligence (AI)
Pages: 320 / 319

Availability: 5000 in stock

SKU: 9781139061773 Categories: ,

Transfer Learning Qiang Yang, Yu Zhang, Wenyuan Dai, Sinno Jialin Pan
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. Categories:
Computers – Artificial Intelligence (AI)
Cambridge University Press
ISBN 10:
ISBN 13:
86 MB


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Transfer Learning

Availability: 5000 in stock