by Michele Laurelli
A technique where knowledge learned from one task is applied to a different but related task, reducing training time and data requirements.
Transfer learning leverages pre-trained models. Common approach: use a model trained on large dataset (e.g., ImageNet) as starting point for specific task.
Using pre-trained ResNet for medical image classification
Fine-tuning BERT for sentiment analysis
Adapting GPT for code generation