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A preview from our curated question bank. The generator produces fresh, AI-tailored questions on each run.
What is the difference between a model parameter and a hyperparameter?
Tip: Parameters are learned from data during training (weights, biases). Hyperparameters are set before training and control the learning process (learning rate, number of layers, batch size). You tune hyperparameters with cross-validation; you do not tune parameters directly.
What is transfer learning and when is it most beneficial?
Tip: Transfer learning uses a model pre-trained on a large dataset as a starting point. Most beneficial when: labelled data is scarce, compute budget is limited, or domains are similar (ImageNet to medical imaging). Fine-tune the last layers; freeze early layers.
Explain backpropagation. What is it actually computing?
Tip: Backpropagation computes the gradient of the loss function with respect to each weight using the chain rule. It propagates error signal from output layer backwards. The gradient tells the optimiser (SGD/Adam) how to adjust each weight to reduce loss.
What is the vanishing gradient problem? How is it addressed in modern deep learning?
Tip: In deep networks with sigmoid/tanh activations, gradients shrink exponentially during backprop — early layers learn very slowly. Solutions: ReLU activations, residual connections (ResNet skip connections), batch normalisation, gradient clipping for RNNs.
What is model drift? How do you detect and handle it?
Tip: Data drift: input distribution shifts over time. Concept drift: the relationship between inputs and output changes. Detect with: monitoring prediction score distributions, input feature statistics, and business KPIs. Handle with: scheduled retraining, online learning.
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