Data Science Deep Learning and Neural Networks 2

Question 1

Which type of neural network architecture is specifically designed to handle sequential data such as time series or text?

Accepted Answer

Recurrent Neural Network (RNN)

Answer

RNNs maintain a hidden state that captures information from previous time steps, making them suited for sequential dependencies.

Question 2

What is the key innovation of Long Short-Term Memory (LSTM) networks compared to standard RNNs?

Accepted Answer

They include gating mechanisms to control information flow and address long-range dependencies

Answer

LSTMs use input, forget, and output gates to selectively retain or discard information, solving the vanishing gradient problem in long sequences.

Question 3

In the context of deep learning, what does 'transfer learning' mean?

Accepted Answer

Using a pre-trained model's learned representations as a starting point for a new task

Answer

Transfer learning reuses weights from a model trained on a large dataset, fine-tuning them on a smaller target dataset to save time and improve accuracy.

Question 4

What is the purpose of batch normalization in a neural network?

Accepted Answer

To normalize layer inputs to reduce internal covariate shift and speed up training

Answer

Batch normalization normalizes activations within each mini-batch, stabilizing training and often allowing higher learning rates.

Question 5

Which loss function is most appropriate for a multi-class classification problem with a softmax output layer?

Accepted Answer

Categorical Cross-Entropy

Answer

Categorical cross-entropy measures the divergence between the predicted probability distribution and the true one-hot encoded label across multiple classes.

Data Science Practice Test

Data Science Practice Test

Data Science Deep Learning and Neural Networks 2