All MCQs

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

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

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

The correct answer is a gan trains a generator and discriminator in an adversarial setting. This matches the Deep Learning course topic: GANs.

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

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

The correct answer is a recurrent neural network processes sequential data using connections across time steps. This matches the Deep Learning course topic: RNN.