Exploring Neural Networks

Exploring Neural Networks

University

20 Qs

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Exploring Neural Networks

Exploring Neural Networks

Assessment

Quiz

Computers

University

Practice Problem

Hard

Created by

Rakesh Rai

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20 questions

Show all answers

1.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

Explain the role of activation functions in neural networks.

Activation functions eliminate the need for weights in a neural network.

Activation functions enable neural networks to learn complex patterns by introducing non-linearity.

Activation functions are used to increase the speed of training.

Activation functions are only necessary in the output layer.

2.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What is the backpropagation algorithm used for?

To generate random weights for neural networks.

To train artificial neural networks by optimizing weights through gradient descent.

To increase the learning rate of a neural network.

To visualize the structure of neural networks.

3.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

Describe the architecture of a Convolutional Neural Network (CNN).

A Convolutional Neural Network uses only fully connected layers without any convolutional operations.

A CNN architecture includes only input and output layers without any hidden layers.

A Convolutional Neural Network (CNN) consists of convolutional layers, activation layers, pooling layers, and fully connected layers.

A CNN is composed solely of linear layers and dropout layers.

4.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What is the Least Mean Square (LMS) algorithm?

The LMS algorithm is a data compression method that reduces file sizes.

The LMS algorithm is a machine learning model that predicts future values without adaptation.

The LMS algorithm is an adaptive filtering technique that minimizes the mean square error.

The LMS algorithm is a static filtering technique that maximizes the mean square error.

5.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What causes the exploding gradient problem in neural networks?

The exploding gradient problem is caused by excessively large gradients during backpropagation in deep neural networks.

Insufficient training data during model training.

Inadequate activation functions in the layers.

Using a shallow network architecture.

6.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

How can the vanishing gradient problem be avoided?

Use ReLU activation, batch normalization, gradient clipping, proper weight initialization, or LSTM/GRU architectures.

Increase learning rate excessively

Ignore weight initialization techniques

Use sigmoid activation functions

7.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What is data augmentation and why is it important?

Data augmentation is a method to artificially expand training datasets by creating modified versions of existing data, which is important for improving model generalization and reducing overfitting.

Data augmentation is a technique to reduce dataset size by removing redundant data.

Data augmentation is a method to analyze data without modifying it.

Data augmentation is a process of collecting new data from external sources.

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