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Rohit Ghumare
dda194f840
Every "Test Your Understanding" quiz placed the correct answer in option B.
Across the 2026 questions in 338 quiz files the correct answer sat at index 1
in 61.5% of cases (uniform would be ~25%), and 107 files had every answer at B,
making the quizzes guessable without reading them.
scripts/debias_quizzes.py rewrites each question's option order with a
deterministic, content-seeded permutation and updates the correct index to
follow the moved answer. It is idempotent: options are canonicalised to a sorted
base before permuting, so re-running produces byte-identical output. Questions
whose options reference each other by position ("all of the above", "both A and
B") are left untouched. The correct-answer value, the option set, and every
explanation are preserved exactly; only order and the index change.
Result: A 23.8% / B 26.3% / C 23.5% / D 26.4%.
The script doubles as a CI guard: `--check` exits non-zero if any quiz is not
de-biased, wired into the curriculum workflow so new lessons cannot regress.
Fixes #368
38 lines
2.5 KiB
JSON
38 lines
2.5 KiB
JSON
[
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{
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"question": "Why can't you build a useful deep network using only linear layers (no activation functions)?",
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"options": ["Stacking linear layers collapses to a single linear transformation, giving no benefit from depth", "Linear layers are too slow", "Linear layers can't handle batched data", "Linear layers don't have biases"],
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"correct": 0,
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"explanation": "y = W2(W1*x + b1) + b2 simplifies to y = Ax + c. No matter how many linear layers you stack, the result is equivalent to one linear layer. Activation functions break this composability.",
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"stage": "pre"
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},
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{
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"question": "What is the output range of the ReLU activation function?",
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"options": ["(-infinity, infinity)", "[0, infinity)", "(0, 1)", "(-1, 1)"],
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"correct": 1,
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"explanation": "ReLU(x) = max(0, x). It outputs 0 for all negative inputs and passes positive inputs unchanged, giving a range of [0, infinity).",
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"stage": "pre"
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},
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{
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"question": "What is the 'dead neuron' problem in ReLU networks?",
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"options": ["Neurons with zero bias", "Neurons that compute too slowly", "Neurons whose input is permanently negative, producing zero output and zero gradient forever", "Neurons that produce NaN values"],
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"correct": 2,
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"explanation": "If a ReLU neuron's weighted input is always negative (due to bad initialization or large negative bias), it outputs 0 with gradient 0. It can never recover because zero gradient means zero update.",
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"stage": "post"
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},
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{
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"question": "Which activation function is the default for hidden layers in modern transformers like GPT and BERT?",
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"options": ["Sigmoid", "Tanh", "GELU", "ReLU"],
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"correct": 2,
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"explanation": "GELU (Gaussian Error Linear Unit) is the default activation in transformers. It provides smooth gradient flow, avoids dead neurons, and has been shown to outperform ReLU in language models.",
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"stage": "post"
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},
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{
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"question": "Why is softmax used only in the output layer and never in hidden layers?",
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"options": ["It only works with two classes", "It causes exploding gradients", "It converts a vector into a probability distribution (values sum to 1), which is needed for classification output but not for intermediate representations", "It's too slow for hidden layers"],
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"correct": 2,
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"explanation": "Softmax normalizes a vector so all values are in (0,1) and sum to 1, creating a probability distribution. Hidden layers need to preserve and transform information, not compress it into probabilities.",
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"stage": "post"
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}
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]
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