Data Science Deep Learning and Neural Networks

Question 1

What is the primary role of an activation function in a neural network?

Accepted Answer

To introduce non-linearity into the model

Answer

To initialize weights

Answer

To normalize input data

Answer

To reduce overfitting

Question 2

Which optimization algorithm adapts the learning rate for each parameter using estimates of first and second moments of the gradients?

Accepted Answer

Adam

Answer

SGD

Answer

RMSProp

Answer

Adagrad

Question 3

What does the term 'epoch' mean in the context of training a neural network?

Accepted Answer

One complete pass through the entire training dataset

Answer

A single weight update step

Answer

A batch of training samples

Answer

The learning rate schedule

Question 4

Which technique randomly drops neurons during training to reduce overfitting in neural networks?

Accepted Answer

Dropout

Answer

Batch normalization

Answer

Weight decay

Answer

Early stopping

Question 5

In a convolutional neural network (CNN), what does a pooling layer primarily do?

Accepted Answer

Reduces spatial dimensions of feature maps

Answer

Adds more learnable parameters

Answer

Applies the activation function

Answer

Connects every neuron to every other neuron

Question 6

What is the vanishing gradient problem in deep neural networks?

Accepted Answer

Gradients shrink exponentially as they propagate back through layers, slowing learning

Answer

Gradients become very large and destabilize training

Answer

The loss function reaches a local minimum too early

Answer

Weights converge to zero due to regularization