Beginner’s Guide To Transfer Learning – How and When to Use?
Transfer learning is a technique for predictive modelling on a different yet similar problem that can then be reused partly or wholly to accelerate its training and eventually improve the performance of the model for the problem. It is the reuse of a pre-trained model on a new problem. This technique is currently becoming very popular in deep learning because it can train deep neural networks with comparatively little data and in less time. Finding its use in the data science field as most real-world problems typically do not have millions of labelled data points to train such complex models. Features from a model that has learned to identify something can become useful to kick-start a model meant to identify another thing.