Definition

Transfer learning is when an AI takes what it learned from one job and uses that knowledge to help with a different, but related, job. It saves time and makes training faster, especially when you don’t have a lot of new data.

Example

An AI trained to recognize cars can use transfer learning to help recognize trucks or buses with less training.

How It’s Used in AI

Used in language models, image recognition, and medical AI. For example, a model trained on general images can be fine-tuned to detect diseases in X-rays with fewer examples. It helps developers build strong models even with limited task-specific data.

Brief History

Transfer learning became popular in the 2010s with deep learning. Models like ImageNet’s pre-trained networks and later BERT showed how powerful pre-trained knowledge could be when adapted to new tasks.

Key Tools or Models

Popular tools include BERT, ResNet, GPT-4, and platforms like Hugging Face Transformers, which allow users to fine-tune large pre-trained models.

Pro Tip

Always pick a base model trained on data close to your new task. The closer the fit, the better the results from transfer learning.