Journal of Artificial Intelligence, Machine Learning and Soft Computing

Abstract

Transfer learning has emerged as a powerful paradigm in machine learning, allowing models to leverage knowledge gained from one task to improve performance on another. This paper explores the principles, techniques, and applications of transfer learning in the context of machine learning, with a focus on the use of pre-trained models. We delve into the advantages, challenges, and best practices associated with transfer learning, presenting a comprehensive overview of its applications in various domains.

Keywords: Transfer Learning, Pre-trained Models, Fine-tuning, Inductive Transfer Learning, Transductive Transfer Learning, Advantages, Challenges, Image Classification, Natural Language Processing, Medical Imaging, Speech Recognition, Robotics.