Deep Learning MCQs With Answers

Computer MCQs 360April 29, 2024

0 21 1 minute read

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

Deep Learning Online Quiz

By presenting 3 options to choose from, Deep Learning Quiz which cover a wide range of topics and levels of difficulty, making them adaptable to various learning objectives and preferences. You will have to read all the given answers of Deep Learning Questions and Answers and click over the correct answer.

Test Name: Deep Learning MCQ Quiz Practice
Type: Quiz Test
Total Questions: 40
Total Marks: 40
Time: 40 minutes

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Deep Learning

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1 / 40

________ is used to optimize neural network weights during training.

Backpropagation

K-means clustering

Gradient descent

2 / 40

________ is a technique used to generate captions for images.

Dimensionality reduction

Clustering

Image captioning

3 / 40

________ is used to evaluate the performance of a model on unseen data.

Model training

Validation set

Feature selection

4 / 40

What is the role of activation functions in Deep Learning?

Optimize loss function

Introduce non-linearity

Normalize data

5 / 40

________ is a type of activation function commonly used in Deep Learning.

ReLU (Rectified Linear Unit)

Tanh

Sigmoid

6 / 40

What does the term "backpropagation" refer to in Deep Learning?

Gradient descent

Optimize loss function

Update model weights

7 / 40

What is the advantage of using Convolutional Neural Networks (CNNs) for image processing tasks?

Optimize loss function

Handle time-series data

Spatial hierarchies

8 / 40

________ is a method to initialize neural network weights effectively.

Regularization

Xavier initialization

Principal component analysis

9 / 40

What is the goal of data augmentation in Deep Learning?

Normalize data

Increase dataset diversity

Reduce model complexity

10 / 40

________ is an architecture used for generating human-like text.

Autoencoder

Transformer

Recurrent Neural Network (RNN)

11 / 40

________ is a technique to generate new data samples from existing data.

K-nearest neighbors

Generative Adversarial Network (GAN)

Naive Bayes

12 / 40

________ is a type of neural network used for time-series forecasting.

Convolutional Neural Network

Recurrent Neural Network (RNN)

Autoencoder

13 / 40

What is the purpose of batch normalization in Deep Learning?

Optimize loss function

Improve convergence

Dimensionality reduction

14 / 40

________ is an architecture used for image generation.

Recurrent Neural Network

Autoencoder

Generative Adversarial Network (GAN)

15 / 40

________ is a technique used to prevent overfitting in Deep Learning.

Batch normalization

Data augmentation

Dropout

16 / 40

What is the purpose of dropout in neural networks?

Normalize data

Prevent overfitting

Optimize loss function

17 / 40

What does the term "deep" refer to in Deep Learning?

Large dataset

Multiple layers

High computational cost

18 / 40

________ is an architecture designed to process sequences of data.

Support Vector Machine (SVM)

Recurrent Neural Network (RNN)

Decision tree

19 / 40

What is the purpose of pooling layers in Convolutional Neural Networks?

Train neural networks

Regularize weights

Downsample feature maps

20 / 40

________ is a type of neural network that excels at image recognition tasks.

Recurrent Neural Network

Convolutional Neural Network

Autoencoder

21 / 40

________ is a method to handle imbalanced datasets in Deep Learning.

Principal component analysis

SMOTE

Regularization

22 / 40

What is the purpose of the Adam optimizer in Deep Learning?

Optimize loss function

Normalize data

Adaptive learning rate

23 / 40

________ is a method to align data distributions between different domains.

Feature selection

Domain adaptation

Model training

24 / 40

What is Deep Learning?

Neural network

Decision tree

Regression analysis

25 / 40

________ is a type of unsupervised learning used for feature learning.

Reinforcement learning

Decision tree

Autoencoder

26 / 40

________ is a technique used to interpret model predictions.

Regularization

Explainable AI

Data augmentation

27 / 40

What is the main advantage of using deep neural networks?

Optimize loss function

Handle categorical data

Learn complex patterns

28 / 40

________ is a technique used to reduce the dimensionality of data.

Support Vector Machine (SVM)

Decision tree

Autoencoder

29 / 40

________ is an architecture designed for handling sequential data.

K-means clustering

Long Short-Term Memory (LSTM)

Decision tree

30 / 40

________ is used to understand the importance of features in Deep Learning models.

Kernel method

Batch normalization

Feature importance

31 / 40

________ is a technique used to update neural network weights layer by layer.

Layer-wise pretraining

Data augmentation

Regularization

32 / 40

________ is a technique used to learn representations from unlabeled data.

Decision tree

Support Vector Machine (SVM)

Self-Supervised Learning

33 / 40

________ is a technique used to train models with less labeled data.

Ensemble learning

Semi-supervised learning

Reinforcement learning

34 / 40

________ is an unsupervised learning technique used for clustering data.

Decision tree

K-means clustering

Gradient boosting

35 / 40

What is the goal of transfer learning in Deep Learning?

Normalize data

Optimize loss function

Transfer knowledge

36 / 40

________ is a technique used to handle vanishing gradient problems.

Regularization

Data augmentation

Gradient clipping

37 / 40

________ is a technique used to reduce the complexity of neural networks.

Ensemble learning

Pruning

Regularization

38 / 40

________ is a technique to improve model generalization in Deep Learning.

Regularization

Feature importance

Dimensionality reduction

39 / 40

________ is an architecture designed for processing sequential data efficiently.

Autoencoder

Transformer

Decision tree

40 / 40

What is the purpose of the loss function in Deep Learning?

Regularize weights

Measure model performance

Train neural networks

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Computer MCQs 360April 29, 2024

0 21 1 minute read