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.
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