Quiz with answers — How a model learns from examples
Answer all eight questions, then check the score. Open only the explanations needed for remediation.
1. Solve the worked-case variant: Replace input 2 with 2.4 while keeping weight 0.4 and target 1. Predict whether error increases or decreases, then recompute prediction and error before any update.
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A. Prediction becomes 0.4×2.4=0.96 and target-minus-prediction error falls from 0.20 to 0.04. The update keeps the same direction but should be smaller because the model is already closer to target. The correct answer executes the requested change and gives a checkable result; the other texts do not close this calculation or trace.
2. Which causal order correctly connects the first three stages of “How a model learns from examples”?
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B. An example and a target → The prediction → Measure the error The chain follows the taught progression; reversing stages consumes a representation or state before it is produced.
3. If “Measure the error” is removed, which diagnostic method is defensible?
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C. Keep the same input, predict the first output that depends on “Measure the error,” then compare the before/after trace. One intervention and a prior prediction make the delta attributable to the removed mechanism.
4. Which verdict respects this session’s validity boundary?
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D. This one-weight loop is a teaching model. Real models have many parameters, gradients, and optimizers. The correct answer bounds the conclusion; the others turn a local relation into a global guarantee.
5. Which evidence best matches the stated status of “How a model learns from examples”?
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A. Established mechanisms; numerical simplifications are pedagogical. Product or mechanism evidence must remain attributed and measured; availability and completion do not prove value.
6. When should a simpler baseline be preferred to “Repeat across many examples”?
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B. When a controlled test shows equivalent quality with lower memory, latency, or complexity. The choice depends on a measured trade-off on the real workload, not novelty or one isolated metric.
7. A learner gets the right result but cannot explain “The prediction.” Which remediation is most useful?
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C. Rebuild the first missing transformation, label its inputs and outputs, then test a neighboring case. The remediation targets the first causal break and then requires transfer instead of rewarding a guessed result.
8. Which submission actually demonstrates the outcome “Run one simple numerical update.”?
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D. A trace with starting data, transformations, observed result, boundary, and next experiment. The correct submission makes the reasoning reproducible and the verdict revisable by future measurement.