# Assessments · attempt before opening the answer key

## Readiness diagnostic

R1. For predictions 2 and 5, with targets 1 and 3, compute MSE.
R2. Why must a fitted scaler use training rows only?
R3. A dataset contains repeated readings from each machine. Why can a random row split mislead?
R4. Write the output shape for a batch of seven rows and a linear layer mapping three features to two outputs.
R5. Is choosing the best test score among ten models a valid final test? Explain.

Readiness is about finding review needs. If two or more answers are missing, revisit Python arrays, Statistics residuals and Applied ML evaluation before starting the training modules.

## Checkpoint A · computation (after Lesson 04)

A1. Distinguish a batch axis from a feature axis using `[16,3]`.
A2. Predict the result shape of `[4,1] - [4]` and explain the likely error.
A3. Why can two affine layers without an activation be replaced by one affine layer?
A4. In the hand-worked network, give dL/dw and explain its sign.
A5. Why use float64 and a small but nonzero finite-difference step for a gradient check?
A6. With 360 training rows and batch size 32, how many updates occur in one complete epoch without dropping rows?

## Checkpoint B · learning (after Lesson 08)

B1. Put zero_grad, forward/loss, backward and step in order and explain each.
B2. What does eval change, and what does no_grad change?
B3. A model's training loss falls but validation loss rises. Give two plausible explanations, one of which is not overfitting.
B4. How is a best-checkpoint model selected in this package, and does that save training computation?
B5. For `L=0.5*w²`, why does SGD learning rate 2.2 produce growing loss?
B6. If a classifier predicts no positives, how does this report define precision? Why should that convention be disclosed?

## Checkpoint C · extension and evidence (after Lesson 12)

C1. Derive the 170-parameter CNN count.
C2. Why does a 90-degree rotation require changing the bars label?
C3. Does freezing weights disable dropout? Explain.
C4. Compute equal-score attention for values `[2,0]` and `[0,4]`.
C5. Does repeating the same test run increase its sample size?
C6. A synthetic test has zero errors out of 120. Name two conclusions this does not establish.

Suggested self-assessment: 2 points per complete explanation, 1 for a correct conclusion with incomplete reasoning, 0 for an incorrect or unsupported conclusion. A checkpoint score is feedback, not automatic certification. Rework any loss-shape, leakage or test-selection mistake before continuing.