Module 2PyTorch Foundations
Autograd and Backpropagation
Understand computational graphs, gradient flow, and backward passes.
Why this module matters
Training only becomes real when you understand where gradients come from and why they disappear, explode, or accumulate.
Prerequisites
- ▸ Tensor operations
- ▸ Basic derivatives
Learning objectives
- ▸ Explain requires_grad, grad_fn, backward, and detach
- ▸ Manually compare symbolic gradients with autograd output
- ▸ Debug accumulation and no_grad issues
Core concepts
Reverse-mode automatic differentiation
Gradient accumulation
Computation graph lifetime
Hands-on practice
- ▸ Differentiate a small polynomial by hand and with PyTorch
- ▸ Inspect grad_fn chains in a simple MLP
- ▸ Break training with in-place ops and then fix it
Expected output
A minimal autograd lab that demonstrates gradient flow and common failure modes.
Study checklist
- ✅ Explain requires_grad, grad_fn, backward, and detach
- ✅ Manually compare symbolic gradients with autograd output
- ✅ Debug accumulation and no_grad issues
Common mistakes
- ⚠️ Forgetting zero_grad
- ⚠️ Calling backward twice on a freed graph
- ⚠️ Using detach where gradients are required
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 gradients clear, the next move is structuring trainable models.
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.