All MCQs

The correct answer is gradient descent updates parameters in the direction that reduces loss. This matches the Deep Learning course topic: Gradient descent.

The correct answer is data augmentation creates modified training examples to improve robustness. This matches the Deep Learning course topic: Data augmentation.

The correct answer is a test set estimates performance on unseen data. This matches the Deep Learning course topic: Test set.

The correct answer is the learning rate controls the step size of parameter updates. This matches the Deep Learning course topic: Learning rate.

The correct answer is data normalization scales or centers data to support stable training. This matches the Deep Learning course topic: Data normalization.

The correct answer is a validation set supports model selection and hyperparameter tuning. This matches the Deep Learning course topic: Validation set.

The correct answer is convolution applies learnable filters to local regions of input data. This matches the Deep Learning course topic: CNN convolution.

The correct answer is dropout randomly disables units during training to reduce co-adaptation. This matches the Deep Learning course topic: Dropout.

The correct answer is generalization is the ability to perform well on unseen examples. This matches the Deep Learning course topic: Generalization.

The correct answer is pooling reduces spatial dimensions while keeping important features. This matches the Deep Learning course topic: CNN pooling.

The correct answer is batch normalization normalizes intermediate activations to stabilize and speed up training. This matches the Deep Learning course topic: Batch normalization.

The correct answer is overfitting occurs when a model learns noise or specific training patterns too strongly. This matches the Deep Learning course topic: Overfitting.