What is "Transfer Learning" and why is it so important today? When AI was being developed, Learning models similar to creating a child each day at the beginning. They needed to learn how to recognize colors, shapes and other objects repeatedly in order to finish every task. It wasn't just long and costly, but also expensive in terms of computational energy. It was followed by Transfer Learning an innovative paradigm which has dramatically enhanced the AI development we are seeing today. Defining Transfer Learning In its simplest terms, Transfer Learning is a method of machine learning where models created for a particular task can be used to design a new model that could be used in an identical project. Instead of creating the neural network through random initialization it is built using an "pre-trained" model which has been working for hours working on millions of images and Terabytes of data. You then can further modify it to meet the needs of your. Imagine you're working on an AI capable of identifying those rarest species of bird that exist. Instead of acquiring 10 million images to show the AI what"the "beak," "wing," or "feather" is the model you build which recognizes these basic shapes using a massive amount of information. Then you "transfer" the information you have gathered to assigning a birds classification. It requires only the smallest amount of information in addition to computing resources, which allows you to reach the highest level of precision. https://www.iteducationcentre.com/artificial-intelligence-training-courses-in-pune