Check your understanding of this module before moving on.
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Checkpoint quiz 3 questions
A quick check on this module's key ideas before you move on
Question 01
Why is cross-entropy loss generally preferred over mean squared error for classification problems with a softmax output?
A is correct. When combined with softmax, cross-entropy's gradient simplifies to (predicted - true), which stays informative even for very wrong, confident predictions. MSE through a softmax can saturate and produce tiny gradients in the same scenario, slowing learning.
Question 02
What does a softmax layer do to a vector of raw output scores (logits)?
B is correct. Softmax turns arbitrary real-valued logits into a probability distribution: it exponentiates each value (so everything becomes positive) and divides by the sum (so they total 1).
Question 03
For a regression task predicting a continuous value, which loss function is the standard default and why?
B is correct. MSE's gradient is proportional to (predicted - actual), so bigger mistakes produce proportionally bigger gradient signals, which is exactly the behavior wanted when predicting a continuous quantity.