Week 04: Exercises & Checklist

Graded exercises

Easy: Run notebooks/01-pytorch-tensors-and-autograd.ipynb to completion. Confirm the autograd gradient matches your hand-computed value and record the toy MLP's final training loss in the Week 4 tracker sheet.
Standard: In notebooks/02-mlp-eta-train-and-eval.ipynb, deliberately break exactly one thing (for example, set the learning rate 100× too high, or skip feature normalization) and run it. In a markdown cell, diagnose the failure from the learning curves (e.g. exploding loss, or training loss far below validation loss) and name the fix.
Stretch: Tune three hyperparameters: hidden width, dropout rate, and weight decay, using the validation split only, and record the best combination's validation MAE. Only then evaluate on the test set exactly once, and report that number alongside the baseline.
Portfolio: Complete the ETA prediction (ML → DL) with model cards milestone (see curriculum/projects/README.md): commit model card v2 with (a) the neural test MAE, (b) the Week 3 baseline MAE it is compared against, and (c) the error-analysis note naming the biggest error cluster by carrier/lane plus a hypothesis.

Easy: Compare your hand-computed 2x value against x.grad.item() right after y.backward(); the toy-MLP final loss is the number printed by the notebook's last cell, and "near zero" means the net learned the linear signal.
Standard: Change exactly one knob (e.g. lr=0.1, or skip the scaler) and re-run; then read the shape of the train/val curves, an exploding value or a diverging gap to name the failure before you fix it.
Stretch: Loop over a small grid of (width, dropout, weight_decay), train each on the training split, and score on validation only; the best combo is the one with the lowest validation MAE, and only then do you run the test set once.
Portfolio: In the error-analysis note, pair the number (the worst carrier/lane's mean absolute error) with the hypothesis (e.g. extreme delays or seasonality dominate that slice), the gate is the number plus the "why," not the number alone.

[ ] Mon: Study tensors, autograd, and the computation-graph mental model.
[ ] Tue: Run the MLP notebook; verify loss curves; deliberately break one thing and diagnose it.
[ ] Wed: Add dropout/weight decay; tune 3 hyperparameters using the validation split.
[ ] Thu: Compare neural vs baseline on the test set; segment errors by carrier and lane.
[ ] Fri: Use case: write the error-analysis note (biggest error cluster + hypothesis).
[ ] Sat: Take the Week 4 quiz (quiz.md), pass with 8/10; record the score in Notes.
[ ] Milestone: Update the Excel tracker; commit model card v2.