Humans have great ability to generalize, we can very efficiently apply the knowledge we learned in classrooms to real world problems. Transfer learning provides a similar capability to artificial neural networks. This talk introduces "Transfer Learning” and how it can be used to reduce training times for different problem domains. We will illustrate using Keras with Tensorflow backend.
The talk introduces the technique of 'Transfer Learning' described by Andrew Ng, the leading expert in Machine Learning as the "next driver of ML commercial success". In the last one decade, deep neural architectures, guided by supervised learning have been the major source of the success of machine learning. These deep neural networks have two key requirements immensely huge labelled data and computationally efficient hardware (GPUs). While such immensely large data exists for some tasks and domains, in most cases the data are usually proprietary or expensive. Transfer Learning, the technique to use models pre-trained on one domain for another problem domain, provides the ability to use DNNs even when the dataset is small. The outline of the talk is: