Transfer Learning & Fine-Tuning

Training from scratch is expensive. Pre-trained models encode rich representations that transfer across tasks — the backbone of modern applied ML.

Learning Objectives

• →Describe feature extraction vs. fine-tuning
• →Explain why lower layers transfer better
• →Implement selective layer freezing
• →Evaluate fine-tuning with limited labeled data

Practice