Module 2 – Backpropagation
What you'll learn
Builds on Module 1
≈6 h · 2 h video · 2 h reading · 2 h coding
- Derive gradients for a 1–2 layer network and draw the computation graph
- Implement gradient checking and validate layer derivatives
- Use torch.autograd to build a tiny autograd toy and inspect backward
Lecture 2 – Backpropagation
Lecture 2.2 – Coding in PyTorch & Linear Regression with Autograd
📚 Resources & Lecture Code
Required reading · course book
The Colab notebook contains the lecture code for Module 2 (backpropagation and PyTorch basics). Follow along by running the cells in order.