Neural networks, honestly

Understand the thing everything else is built on.

1. What a neuron actually computes Compute one neuron's output by hand: weights, sum, bias, activation.
2. Layers and activation functions Say why a network without activations collapses to a single layer.
3. Backpropagation, intuitively Explain how blame for an error is shared backwards through the network.
4. Build a neural network from scratch Write a working network and its backward pass in plain Python.
5. PyTorch, first steps Rebuild the same network in PyTorch and see what the framework does for you.

Ends with a graded checkpoint: 8 questions, pass mark 70%.