Phase 0: Vision Foundations · ~75 minutes · Python
Transfer Learning & Fine-Tuning
Somebody else spent a million GPU hours teaching a network what edges, textures, and object parts look like. You should borrow those features before training your own.
Hiring signal: Understanding of transfer learning & fine-tuning internals
What you will learn
- Distinguish feature extraction from fine-tuning and pick the right one based on dataset size, domain distance, and compute budget
- Load a pretrained backbone, replace its classifier head, and train only the head to a working baseline in under 20 lines
- Progressively unfreeze layers with discriminative learning rates so early generic features get smaller updates than late task-specific ones
- Diagnose the three common failures: feature drift from too-high LR on unfrozen blocks, BN statistics collapse on tiny datasets, and catastrophic forgetting
Introduction
Type: Build Languages: Python Prerequisites: Phase 4 Lesson 03 (CNNs), Phase 4 Lesson 04 (Image Classification) Time: ~75 minutes
Learning Objectives
- Distinguish feature extraction from fine-tuning and pick the right one based on dataset size, domain distance, and compute budget
- Load a pretrained backbone, replace its classifier head, and train only the head to a working baseline in under 20 lines
- Progressively unfreeze layers with discriminative learning rates so early generic features get smaller updates than late task-specific ones
- Diagnose the three common failures: feature drift from too-high LR on unfrozen blocks, BN statistics collapse on tiny datasets, and catastrophic forgetting
Unlock the full lesson
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