All MCQs

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 evaluation metrics such as accuracy, precision, recall, and loss help compare models. This matches the Deep Learning course topic: Evaluation metrics.

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 standard benchmarks help compare architectures on common tasks and datasets. This matches the Deep Learning course topic: Benchmarks.

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 a feature map represents filter responses across spatial locations. This matches the Deep Learning course topic: CNN feature maps.

The correct answer is dropconnect randomly drops weights rather than neuron outputs during training. This matches the Deep Learning course topic: DropConnect.

The correct answer is underfitting occurs when a model is too simple to capture important patterns. This matches the Deep Learning course topic: Underfitting.