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Why and How Enterprises are adopting Transfer learning ?

We are seeing that there is an Enterprise-wide adoption of transfer learning techniques while using Machine learning and Deep learning. And here is the reason why…

Traditionally, all the Machine learning algorithms assume learning to happen from scratch for every new learning problem. The assumption is that no previous learning will be leveraged. Transfer learning is distinct from other approaches in learning cycle time and improving performance of the models. In cases where the domains for the learning problems relate, there will be some learnings from the past that can be acquired and used. Some common examples include:

  1. The knowledge of French could help students learn Spanish

  2. The knowledge in mathematics could help students learn Physics

  3. The knowledge of driving a car could help drivers learn to drive the truck

  4. The knowledge of recognizing cats could help computers to recognize tigers

In the Machine learning context, this refers to identifying and applying the knowledge accumulated from previous tasks to new tasks from a related domain. The key here is the ability to identify the commonality between the domains. Reinforcement learning and classification and regression problems apply transfer learning. The transfer learning process flow is as shown here: