What is Transfer Learning?
Transfer learning is a machine learning method where a model developed for a task is reused as the starting point for a model on a second task.
Transfer Learning differs from traditional Machine Learning in that it is the use of pre-trained models that have been used for another task to jump start the development process on a new task or problem.
Transfer learning involves the concepts of a domain and a task. A domain DD consists of a feature space XX and a marginal probability distribution P(X)P(X) over the feature space, where X=x1,⋯,xn∈XX=x1,⋯,xn∈X. For document classification with a bag-of-words representation, XX is the space of all document representations, xixi is the ii-th term vector corresponding to some document and XX is the sample of documents used for training.
(https://cdn-images-1.medium.com/max/1600/1*Pj6I0mkWQFeNyMf0JYYPeA.png)
The benefits of Transfer Learning are that it can speed up the time it takes to develop and train a model by reusing these pieces or modules of already developed models. This helps speed up the model training process and accelerate results.
Source: medium