Deep Learning MCQs

The correct answer is backpropagation through time unfolds an rnn across time steps to compute gradients. This matches the Deep Learning course topic: BPTT.

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 lstm networks use gates to manage long-term dependencies and reduce vanishing-gradient effects. This matches the Deep Learning course topic: LSTM.

The correct answer is a loss function measures the mismatch between predictions and targets. This matches the Deep Learning course topic: Loss function.

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 gpus accelerate deep learning by performing many parallel numerical operations. This matches the Deep Learning course topic: GPU programming.

The correct answer is deep learning learns layered representations from raw data. This matches the Deep Learning course topic: Basics of deep learning.

The correct answer is cnn computational cost depends on filter size, input size, channels, and number of filters. This matches the Deep Learning course topic: CNN complexity.

The correct answer is resnet uses skip connections to help train very deep networks. This matches the Deep Learning course topic: ResNet.

The correct answer is cudnn provides optimized gpu routines for deep neural network operations. This matches the Deep Learning course topic: CuDNN.