Neural Network Models MCQs With Answers

Computer MCQs 360April 10, 2024

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Welcome to the Neural Network Models MCQs with Answers. In this post, we have shared Neural Network Models Online Test for different competitive exams. Find practice Neural Network Models Practice Questions with answers in Computer Tests exams here. Each question offers a chance to enhance your knowledge regarding Neural Network Models.

Neural Network Models Online Quiz

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Test Name: Neural Network Models MCQ Quiz Practice
Type: Quiz Test
Total Questions: 40
Total Marks: 40
Time: 40 minutes

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Neural Network Models

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1 / 40

________ networks use attention mechanisms for focusing on important features.

Transformer

LSTM

GANs

2 / 40

________ networks are designed to handle dependencies between variables.

GANs

CNNs

LSTM

3 / 40

________ networks use reinforcement learning techniques to optimize actions.

CNNs

Deep Q-Networks (DQN)

Autoencoders

4 / 40

________ are designed for image classification tasks.

GANs

RNNs

CNNs

5 / 40

The activation function that maps input values to probabilities in a multi-class classification neural network is _________.

Tanh

ReLU

Softmax

6 / 40

Feedforward Neural Networks (FNNs) are primarily used for _________.

Clustering

Classification

Regression

7 / 40

Long Short-Term Memory (LSTM) networks are designed to overcome ________ in RNNs.

Vanishing gradients

Underfitting

Overfitting

8 / 40

________ networks are effective for learning embeddings from text data.

CNNs

Autoencoders

Word2Vec

9 / 40

Residual connections in ResNet help address ________ during training.

Overfitting

Underfitting

Vanishing gradients

10 / 40

________ networks are effective for processing time series data.

GANs

CNNs

RNNs

11 / 40

Batch Normalization is used to ________ during neural network training.

Increase learning rate

Decrease convergence

Stabilize learning

12 / 40

The primary advantage of using CNNs over fully connected networks for image processing is ________.

Global connectivity

Memory usage

Local connectivity

13 / 40

________ networks are designed to predict continuous outputs.

Generative networks

Classification networks

Regression neural networks

14 / 40

A multi-layer perceptron (MLP) consists of ________ layers.

Hidden, Output

Input, Output

Input, Hidden, Output

15 / 40

________ architectures are capable of handling variable-length inputs and outputs.

MLPs

CNNs

Sequence-to-sequence networks

16 / 40

The term "batch size" in neural networks refers to ________.

Number of layers

Number of neurons

Number of samples processed per iteration

17 / 40

Autoencoders are commonly used for ________ tasks.

Classification

Regression

Dimensionality reduction

18 / 40

The activation function used in the output layer of a binary classification neural network is _________.

ReLU

Tanh

Sigmoid

19 / 40

Recurrent Neural Networks (RNNs) are effective for handling ________ data.

Image

Static

Sequential

20 / 40

The sigmoid activation function is used in ________ neural networks.

Regression

Sequence-to-sequence

Binary classification

21 / 40

The primary purpose of a decoder in an autoencoder is to ________.

Reconstruct input

Classify data

Extract features

22 / 40

Gated Recurrent Unit (GRU) networks simplify the LSTM architecture by combining ________ gates.

Input, Output

Reset, Update

Forget, Update

23 / 40

The ResNet architecture introduces ________ connections to improve training.

Recurrent

Skip

Pooling

24 / 40

The Adam optimizer combines ________ and ________ for efficient gradient descent.

Adadelta, Adam

Momentum, RMSprop

SGD, Adagrad

25 / 40

The Gated Recurrent Unit (GRU) simplifies the LSTM architecture by combining ________ gates.

Forget, Update

Reset, Update

Input, Output

26 / 40

The objective of a variational autoencoder (VAE) is to learn ________ representations.

Sparse

Dense

Latent

27 / 40

Generative Adversarial Networks (GANs) consist of ________ and ________ networks.

Encoder, Decoder

Actor, Critic

Generator, Discriminator

28 / 40

The activation function commonly used in hidden layers of neural networks is _________.

Tanh

ReLU

Sigmoid

29 / 40

________ are used to model the uncertainty in predictions of neural networks.

Gradient descent

Dropout networks

Bayesian neural networks

30 / 40

________ are used to reduce the complexity of data before feeding it into neural networks.

Preprocessing techniques

Clustering methods

Ensemble methods

31 / 40

The term "epoch" in neural network training refers to ________.

One backpropagation pass

One pass through the entire dataset

One forward pass

32 / 40

In neural networks, the term "backpropagation" refers to ________.

Updating weights

Forward pass

Activation function

33 / 40

Transfer learning in neural networks involves ________.

Random initialization

Pre-trained models

Dropout regularization

34 / 40

The learning rate in neural networks controls ________.

Number of layers

Activation functions

Gradient descent speed

35 / 40

The softmax activation function is commonly used in the ________ layer of a neural network.

Hidden

Output

Input

36 / 40

The term "padding" in CNNs refers to ________.

Adding zeros around input

Adjusting learning rate

Reducing filter size

37 / 40

________ are designed for processing sequences of data.

CNNs

GANs

RNNs

38 / 40

Dropout is a regularization technique used to prevent ________.

Overfitting

Underfitting

Vanishing gradients

39 / 40

Capsule Networks aim to address issues with ________ in traditional CNNs.

Spatial relationships

Overfitting

Dropout

40 / 40

Convolutional Neural Networks (CNNs) are specialized for ________ tasks.

Image recognition

Time series forecasting

Speech recognition

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Computer MCQs 360April 10, 2024

0 5 1 minute read