6.2.1 PyTorch Roadmap: Tensor, Autograd, Module, DataLoader, Loop

PyTorch is the framework that turns the deep learning loop into runnable code. First learn the execution order; details become easier afterward.

Look at the Workflow First

tensor -> model -> loss -> backward -> optimizer.step -> repeat

Run Autograd Once

Create pytorch_first_loop.py and run it after installing torch.

import torch

w = torch.tensor([0.0], requires_grad=True)
learning_rate = 0.2

for step in range(1, 5):
    loss = (w - 3).pow(2)
    loss.backward()
    with torch.no_grad():
        w -= learning_rate * w.grad
        w.grad.zero_()
    print(step, "w=", round(w.item(), 3), "loss=", round(loss.item(), 3))

Expected output:

1 w= 1.2 loss= 9.0
2 w= 1.92 loss= 3.24
3 w= 2.352 loss= 1.166
4 w= 2.611 loss= 0.42

The key PyTorch habit is visible here: compute loss, call backward(), update without tracking gradients, then clear old gradients.

Learn in This Order

Order	Read	What to practice
1	6.2.2 sklearn to PyTorch Bridge	why the loop becomes explicit
2	6.2.3 PyTorch Basics	tensors, dtype, shape, device
3	6.2.4 Autograd	requires_grad, backward, grad
4	6.2.5 nn Module	model class, parameters
5	6.2.6 Data Loading	Dataset, DataLoader, batch
6	6.2.7 Training Loop	train/eval loop, loss log
7	6.2.8 Practical Tips	shape, device, seed, debugging
8	6.2.9 PyTorch Workshop	run and visualize a tiny model

Pass Check

You pass this roadmap when you can read a PyTorch loop and locate these five things: data batch, model output, loss, backward(), and optimizer update.