Day 73 of 100daysofcode challenge: Transfer learning
What is Transfer Learning?
Transfer learning is a technique where a pre-trained model (usually on a large dataset) is adapted for a new, but related, task. Instead of training a model from scratch, transfer learning leverages knowledge gained from solving one problem to help solve another. This is especially useful when you have limited data for the new task but want to take advantage of the model’s prior learning.
How transfer learning works:
Pre-train on a large dataset: A model is initially trained on a large, general-purpose dataset. For example, in computer vision, a model might be pre-trained on ImageNet, which contains millions of labeled images across many categories.
Adapt to a new task: The pre-trained model is then fine-tuned or modified to work on a different but related task, usually with a smaller dataset. For example, if the model was trained on a large set of general images, it could be adapted to classify medical images or identify specific objects in a new domain.
This technique is powerful because it saves time, reduces the amount of data needed for the new task, and often leads to better performance than training from scratch.