wehub-resource-sync
38 lines
2.9 KiB
JSON
38 lines
2.9 KiB
JSON
[
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{
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"question": "What is overfitting in neural networks?",
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"options": ["The model is too small to learn the data", "The model memorizes training data instead of learning generalizable patterns, showing a large gap between train and test accuracy", "The model trains too slowly", "The loss function is wrong"],
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"correct": 1,
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"explanation": "Overfitting occurs when a model achieves high training accuracy but poor test accuracy. It has memorized the training data's noise rather than learning the underlying patterns.",
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"stage": "pre"
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},
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{
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"question": "How does dropout regularize a neural network?",
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"options": ["It removes the worst-performing neurons permanently", "It randomly zeroes neurons during training, forcing the network to learn redundant representations", "It reduces the learning rate", "It removes outliers from the training data"],
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"correct": 1,
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"explanation": "During each forward pass, dropout randomly sets neuron outputs to zero with probability p. This prevents co-adaptation (neurons relying on specific others) and is equivalent to training an ensemble of 2^N subnetworks.",
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"stage": "pre"
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},
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{
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"question": "Why do transformers use LayerNorm instead of BatchNorm?",
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"options": ["LayerNorm is faster to compute", "LayerNorm normalizes across features per sample (batch-independent), which works with variable sequence lengths and small batch sizes", "BatchNorm causes gradient explosion", "LayerNorm was invented more recently"],
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"correct": 1,
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"explanation": "BatchNorm depends on batch statistics, which are noisy with small batches and meaningless with batch size 1 (common during generation). LayerNorm normalizes across features within each sample, independent of batch size.",
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"stage": "post"
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},
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{
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"question": "What is the key difference between RMSNorm and LayerNorm?",
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"options": ["RMSNorm uses batch statistics", "RMSNorm skips the mean subtraction, only dividing by the root mean square, giving ~10% speedup with equal accuracy", "RMSNorm adds learnable parameters", "RMSNorm only works on CNNs"],
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"correct": 1,
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"explanation": "RMSNorm removes the mean subtraction step from LayerNorm, which contributes little to accuracy but adds computation. LLaMA, Mistral, and most modern LLMs use RMSNorm for this efficiency gain.",
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"stage": "post"
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},
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{
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"question": "Why is it critical to call model.eval() before running inference in PyTorch?",
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"options": ["It speeds up computation", "It disables dropout and switches BatchNorm to use running statistics instead of batch statistics, giving deterministic outputs", "It frees GPU memory", "It enables gradient computation"],
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"correct": 1,
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"explanation": "Without model.eval(), dropout randomly zeroes neurons during inference (causing random output variation) and BatchNorm uses current-batch statistics instead of the stable running averages accumulated during training.",
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"stage": "post"
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}
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]
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