Module 5PyTorch Foundations
Data Pipeline Design
Build datasets and loaders that support real training loops.
Why this module matters
Many model problems are actually data problems: bad labels, wrong normalization, hidden leakage, and slow input pipelines.
Prerequisites
- ▸ PyTorch basics
- ▸ Basic file handling
Learning objectives
- ▸ Write custom Dataset classes
- ▸ Use transforms and collate_fn intentionally
- ▸ Profile input throughput and worker behavior
Core concepts
Dataset vs DataLoader
Batching and shuffling
Augmentation and leakage
Hands-on practice
- ▸ Create a small image dataset loader
- ▸ Implement train/test transforms
- ▸ Visualize batches before training starts
Expected output
A reusable data pipeline template for later projects.
Study checklist
- ✅ Write custom Dataset classes
- ✅ Use transforms and collate_fn intentionally
- ✅ Profile input throughput and worker behavior
Common mistakes
- ⚠️ Applying train augmentation to validation data
- ⚠️ Using too many workers blindly
- ⚠️ Never inspecting a batch visually
Module rhythm
- 1. Read the summary and why-it-matters section first.
- 2. Work through concepts before rushing into practice.
- 3. Use the checklist to verify real understanding, not just completion.
How to continue
With data flowing correctly, it is time to build a training loop worth keeping.
Back to course overview →How to use this page well
Treat each module as a compact learning system: understand the intuition, verify the concepts, do one hands-on task, then use the checklist and mistakes section to pressure-test your understanding.