An Introduction to Transfer Learning in Deep Learning: A Very Short Introduction for Beginners

Transfer Learning is a machine learning technique that enables the transfer of knowledge from a source task to a related target task. It is used to save time and computational resources in training deep learning models by leveraging existing models that have already been trained on large amounts of data. Deep learning models require large amounts of data and computational resources to train. Transfer learning allows us to overcome these limitations by reusing knowledge learned from existing models and fine-tuning them to solve a new task. The process of transfer learning in deep learning can be broken down into two main steps: reusing pre-trained models and fine-tuning pre-trained models.