Introduction to Transfer Learning with NLP

A brief look at Transfer Learning for NLP tasks

Introduction to Transfer Learning

A model created for one task is used as the basis for another using the machine learning technique known as transfer learning.

Pre-trained models are frequently utilized as the foundation for Deep learning tasks in computer vision and natural language processing because they save time and money compared to developing neural network models from scratch and perform vastly better on related tasks. On the other hand, transfer learning is the use of skills acquired while completing one activity to assist in the solution of another related challenge. To make machine learning as human-like as possible, machine learning specialists are working to create algorithms that facilitate transfer learning processes.

Algorithms for machine learning are often created to handle discrete tasks. Methods to transfer information from one or more of these source tasks to enhance learning in a related target task are developed through transfer learning. This exchange of learning techniques aims to advance machine learning and make it as effective as human learning.

Transfer learning uses three different methods to resolve deep learning issues.