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Transfer Learning
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Book description

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.

Reviews

'Transfer learning is a critically important approach in settings where data is sparse or expensive. This comprehensive text focuses on when to transfer, what to transfer, and how to transfer previously learned knowledge into a novel current task. The authors cover historic methods as well as very recent methods, classifying them into a comprehensive ontology of transfer learning methods. Through its coverage of basic methods, advanced methods, and multiple application domains, the text will provide a useful guide to both novice and the experienced researchers and practitioners.'

Matthew E. Taylor - Principal Researcher at Borealis AI, Edmonton

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