All MCQs

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 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 a multilayer perceptron uses layers of neurons with nonlinear activations. This matches the Deep Learning course topic: MLP.

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 an autoencoder learns to encode input into a latent representation and reconstruct it. This matches the Deep Learning course topic: Autoencoders.

The correct answer is googlenet/inception uses parallel filter operations to capture features at multiple scales. This matches the Deep Learning course topic: GoogleNet/Inception.

The correct answer is activation functions introduce nonlinearity into neural networks. This matches the Deep Learning course topic: Activation functions.

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

The correct answer is sparse coding encourages representations where only a small number of units are active. This matches the Deep Learning course topic: Sparse coding.

The correct answer is deep learning can be applied to image classification, detection, and segmentation. This matches the Deep Learning course topic: Computer vision applications.

The correct answer is backpropagation computes gradients by applying the chain rule through the network. This matches the Deep Learning course topic: Backpropagation.