Day 46 - Transfer Learning Fundamentals

Date: 2025-11-10 (Monday)
Status: “Done”

Transfer Learning: Why It Matters

Classical training starts from scratch for every task. Transfer learning reuses a pre-trained model so you converge faster, get better accuracy, and need less labeled data.

Pipelines Compared

  • Classical: data -> random init model -> train -> predict
  • Transfer: pre-train on large corpus -> reuse weights -> fine-tune on target task -> predict
[Large unlabeled/labeled data] --pre-train--> [Base model weights]
                                      \
                                       fine-tune on task data --> deploy

Two Approaches

  • Feature-based: treat pre-trained embeddings as fixed features; train a new head.
  • Fine-tuning: update (part of) the base model weights on downstream data.

Benefits Checklist

  • Faster convergence because weights are warm-started.
  • Better predictions from richer representations.
  • Smaller labeled datasets needed; leverage unlabeled pre-training.

Key Considerations

  • Domain shift: choose pre-training data close to target domain when possible.
  • Catastrophic forgetting: use smaller learning rate or freeze early layers.
  • Evaluation: monitor if freezing vs. full fine-tuning impacts overfitting.

Practice Targets for Today

  • Sketch a transfer pipeline for your QA task (data, base model, head, metrics).
  • Decide which layers to freeze vs. fine-tune.
  • Prepare a small experiment plan comparing feature-based vs. fine-tuned runs.