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.