Artificial IntelligenceBig Data

What is the Beauty of Transfer Learning?

Beauty of machine learning lies in its power to generalize. Generalization is the algorithm’s ability to learn patterns in a given dataset and predict results. For example, if an algorithm has been trained to predict mortgage price based on a borrower’s credit history and annual salary, that information can be used to predict any new mortgage prices. However, if the same machine learning algorithm is used to predict car insurance price, it will fail. The developer will have to start the process all over again. They will need to acquire a new set of labelled training data, understand the insurance domain, build and train their algorithm and finally deploy it to production.

Supervised learning process described above is computationally and financially expensive to extend into another domain. So, how can a company without enough resources utilize the power of machine learning to build an algorithm and make predictions in a different domain? Luckily, there is Transfer Learning, an algorithm design methodology [1] where an algorithm that has been trained to understand knowledge from one domain can be applied to a different domain.

This is a developing field and Dr. Andrew Ng, former Chief Scientist at Baidu, states that it will be the “next driver …

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