Module 4PyTorch Foundations
Losses, Optimizers, and Schedulers
Learn how objectives and update rules shape model behavior.
Why this module matters
A good architecture can still fail under the wrong optimizer, learning rate, or objective.
Prerequisites
- ▸ Autograd
- ▸ Basic probability
Learning objectives
- ▸ Choose common losses correctly
- ▸ Compare SGD, Adam, and AdamW
- ▸ Understand warmup, cosine decay, and schedule timing
Core concepts
Cross-entropy and regression losses
Adaptive optimization
Learning rate scheduling
Hands-on practice
- ▸ Train one model with three optimizers
- ▸ Plot loss and learning-rate curves
- ▸ Show one bad schedule and explain the failure
Expected output
A short benchmark note on optimization tradeoffs for a small model.
Study checklist
- ✅ Choose common losses correctly
- ✅ Compare SGD, Adam, and AdamW
- ✅ Understand warmup, cosine decay, and schedule timing
Common mistakes
- ⚠️ Confusing logits with probabilities
- ⚠️ Stepping the scheduler at the wrong frequency
- ⚠️ Using weight decay as if it were generic regularization magic
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
The next module turns isolated training code into a real data pipeline.
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.