Module 6 · PyTorch Deep Learning

Module 6: Optimization & Tuning

Optimize model loss via Adam and SGD. Configure decay rates, learning rate scheduler steps, dropout matrices, and batchnorm.

⏱ 25 Min Read • Author: GenAIWallah Team • Updated: May 2026

Day 11

Optimization Algorithms: SGD vs. Adam

Why this matters

Optimizers: Adam adapts learning rates per parameter; SGD with momentum remains a strong baseline.

SGD updates weights along negative gradient; Adam maintains per-parameter momentum and variance estimates (adaptive LR).

Start with Adam (lr=1e-3) for quick experiments.
Learning rate schedulers (StepLR, CosineAnnealingLR) improve convergence.
weight_decay in AdamW adds L2 regularization.

Common mistakes

Forgetting optimizer.zero_grad() so gradients accumulate across batches.
Tensor shape mismatches (especially batch/channel dimensions for CNNs).
Training on GPU but leaving tensors on CPU (or vice versa).

Interview checkpoints

Q: Explain optimizers in PyTorch. A: One-sentence definition + shape/device note.
Q: Common bug? A: Gradients, shapes, or device mismatch.

Practice

Basic: Define Optimizers and sketch a minimal code snippet.
Intermediate: Run a notebook cell demonstrating Optimizers.
Advanced: Intentionally break Optimizers and interpret the error.

Recap

You can explain optimizers clearly.
You know one mistake to avoid.
You see how this connects to the next lesson.

Next: Regularization

Day 12

Regularization and Tuning Techniques

Why this matters

Regularization: Dropout and BatchNorm reduce overfitting and stabilize training — standard in vision models.

Dropout randomly zeros activations during training (disabled in model.eval()). BatchNorm normalizes activations per channel using batch statistics.

Use model.train() vs model.eval() to toggle dropout/BN behavior.
Pair with early stopping on validation loss.

Common mistakes

Forgetting optimizer.zero_grad() so gradients accumulate across batches.
Tensor shape mismatches (especially batch/channel dimensions for CNNs).
Training on GPU but leaving tensors on CPU (or vice versa).

Interview checkpoints

Q: Explain regularization in PyTorch. A: One-sentence definition + shape/device note.
Q: Common bug? A: Gradients, shapes, or device mismatch.

Practice

Basic: Define Regularization and sketch a minimal code snippet.
Intermediate: Run a notebook cell demonstrating Regularization.
Advanced: Intentionally break Regularization and interpret the error.

Recap

You can explain regularization clearly.
You know one mistake to avoid.
You see how this connects to the next lesson.

Next: CNN Layers

← Module 5: GPU Acceleration Module 7: CNNs Classifier →