Deep Learning MCQs With Answers

Computer MCQs 360April 29, 2024

0 9 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

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

Optimize loss function

Update model weights

Gradient descent

2 / 40

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

Optimize loss function

Normalize data

Adaptive learning rate

3 / 40

________ is an architecture used for image generation.

Generative Adversarial Network (GAN)

Autoencoder

Recurrent Neural Network

4 / 40

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

Feature importance

Batch normalization

Kernel method

5 / 40

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

Model training

Feature selection

Validation set

6 / 40

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

Naive Bayes

K-nearest neighbors

Generative Adversarial Network (GAN)

7 / 40

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

Multiple layers

High computational cost

Large dataset

8 / 40

________ is a technique used to generate captions for images.

Dimensionality reduction

Image captioning

Clustering

9 / 40

What is the purpose of batch normalization in Deep Learning?

Dimensionality reduction

Improve convergence

Optimize loss function

10 / 40

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

Domain adaptation

Model training

Feature selection

11 / 40

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

Regularize weights

Measure model performance

Train neural networks

12 / 40

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

Support Vector Machine (SVM)

Autoencoder

Decision tree

13 / 40

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

Data augmentation

Dropout

Batch normalization

14 / 40

What is the role of activation functions in Deep Learning?

Introduce non-linearity

Normalize data

Optimize loss function

15 / 40

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

Data augmentation

Regularization

Layer-wise pretraining

16 / 40

What is the goal of data augmentation in Deep Learning?

Reduce model complexity

Increase dataset diversity

Normalize data

17 / 40

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

Recurrent Neural Network (RNN)

Autoencoder

Transformer

18 / 40

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

Tanh

Sigmoid

ReLU (Rectified Linear Unit)

19 / 40

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

Convolutional Neural Network

Autoencoder

Recurrent Neural Network

20 / 40

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

Pruning

Ensemble learning

Regularization

21 / 40

________ is an architecture designed for processing sequential data efficiently.

Decision tree

Autoencoder

Transformer

22 / 40

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

Self-Supervised Learning

Support Vector Machine (SVM)

Decision tree

23 / 40

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

Regularize weights

Train neural networks

Downsample feature maps

24 / 40

________ is a method to initialize neural network weights effectively.

Principal component analysis

Xavier initialization

Regularization

25 / 40

What is the goal of transfer learning in Deep Learning?

Optimize loss function

Transfer knowledge

Normalize data

26 / 40

What is the purpose of dropout in neural networks?

Prevent overfitting

Optimize loss function

Normalize data

27 / 40

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

Reinforcement learning

Decision tree

Autoencoder

28 / 40

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

Ensemble learning

Semi-supervised learning

Reinforcement learning

29 / 40

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

Principal component analysis

SMOTE

Regularization

30 / 40

________ is a technique used to handle vanishing gradient problems.

Gradient clipping

Regularization

Data augmentation

31 / 40

What is Deep Learning?

Regression analysis

Neural network

Decision tree

32 / 40

________ is used to optimize neural network weights during training.

Backpropagation

Gradient descent

K-means clustering

33 / 40

________ is an unsupervised learning technique used for clustering data.

K-means clustering

Gradient boosting

Decision tree

34 / 40

________ is an architecture designed for handling sequential data.

Decision tree

K-means clustering

Long Short-Term Memory (LSTM)

35 / 40

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

Feature importance

Regularization

Dimensionality reduction

36 / 40

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

Handle time-series data

Spatial hierarchies

Optimize loss function

37 / 40

________ is an architecture designed to process sequences of data.

Recurrent Neural Network (RNN)

Decision tree

Support Vector Machine (SVM)

38 / 40

What is the main advantage of using deep neural networks?

Optimize loss function

Handle categorical data

Learn complex patterns

39 / 40

________ is a technique used to interpret model predictions.

Explainable AI

Regularization

Data augmentation

40 / 40

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

Autoencoder

Recurrent Neural Network (RNN)

Convolutional Neural Network

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Computer MCQs 360April 29, 2024

0 9 1 minute read