Transfer Learning Explained for Advanced Users

Transfer learning is a technique in machine learning where a model trained for a specific task is transferred to another, related task.

The goal is to reuse the features and patterns already learned to speed up or improve training for new tasks.

Typically, a pretrained model is used as a starting point and then fine-tuned for the new task.

Transfer learning is especially useful when only a few training data are available for the new task.

It is applied in areas such as image recognition, speech processing, or robotics, where similar tasks often share similar features.