Transfer Learning

Machine Learning (ML) involves data analysis and enables the system to improve and learn from experience without explicit programming required constantly. There have been many ML approaches that came into existence constantly. Supervised learning was a game-changing approach that was adopted widely across many industries. However, a few limitations of supervised learning can be overcome with the onset of various other approaches. Transfer Learning is a method under research in Machine Learning that stores the knowledge obtained from solving one problem and uses it to solve problems that are different but related to the solved one. Since training a model takes more computational power, time, and data, Transfer Learning helps reduce the same while improving learning accuracy. The target learner learns from the model, which is already trained initially by using the stored knowledge.