DSE DSE - Data Science Deep Learning and Neural Networks 2

Question 1

Which neural network architecture processes sequential data by maintaining a hidden state that captures information from previous time steps?

Accepted Answer

Recurrent Neural Network (RNN)

Answer

RNNs have recurrent connections that pass the hidden state from one time step to the next, enabling them to model temporal dependencies in sequential data like text or time series.

Question 2

What is transfer learning in deep learning?

Accepted Answer

Reusing a model pretrained on one task as the starting point for training on a related task

Answer

Transfer learning leverages features learned by a model on a large source task (e.g., ImageNet) and fine-tunes it on a smaller target task, dramatically reducing data and compute requirements.

Question 3

What does batch normalization do in a neural network?

Accepted Answer

Normalizes the inputs to each layer to have zero mean and unit variance within each mini-batch

Answer

Batch normalization standardizes layer inputs within each mini-batch, stabilizing training, reducing sensitivity to weight initialization, and allowing higher learning rates.

Question 4

What is the primary function of a pooling layer in a Convolutional Neural Network?

Accepted Answer

Downsampling feature maps to reduce spatial dimensions and computation

Answer

Pooling layers (e.g., max pooling) reduce the height and width of feature maps by aggregating values in local regions, decreasing computation and providing some translation invariance.

Question 5

What is an autoencoder primarily used for in deep learning?

Accepted Answer

Unsupervised dimensionality reduction and feature learning by learning to compress and reconstruct data

Answer

An autoencoder trains an encoder to compress input into a lower-dimensional latent representation and a decoder to reconstruct the original input, learning compact data representations without labels.

DSE - Data Science Practice Test

DSE - Data Science Practice Test

DSE DSE - Data Science Deep Learning and Neural Networks 2