Machine Learning

Neural Network Models Quiz With Answers

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

By presenting 3 options to choose from, Neural Network Models 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 Neural Network Models Questions and Answers and click over the correct answer.

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

Note: Answer of the questions will change randomly each time you start the test. Practice each quiz test at least 3 times if you want to secure High Marks. Once you are finished, click the View Results button. If any answer looks wrong to you in Quizzes. simply click on question and comment below that question. so that we can update the answer in the quiz section.

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Neural Network Models

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1 / 40

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

2 / 40

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

3 / 40

The sigmoid activation function is used in ________ neural networks.

4 / 40

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

5 / 40

________ are designed for image classification tasks.

6 / 40

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

7 / 40

The learning rate in neural networks controls ________.

8 / 40

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

9 / 40

Autoencoders are commonly used for ________ tasks.

10 / 40

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

11 / 40

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

12 / 40

Batch Normalization is used to ________ during neural network training.

13 / 40

Transfer learning in neural networks involves ________.

14 / 40

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

15 / 40

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

16 / 40

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

17 / 40

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

18 / 40

________ are designed for processing sequences of data.

19 / 40

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

20 / 40

Residual connections in ResNet help address ________ during training.

21 / 40

________ networks use attention mechanisms for focusing on important features.

22 / 40

Dropout is a regularization technique used to prevent ________.

23 / 40

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

24 / 40

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

25 / 40

________ networks use reinforcement learning techniques to optimize actions.

26 / 40

The ResNet architecture introduces ________ connections to improve training.

27 / 40

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

28 / 40

________ networks are designed to predict continuous outputs.

29 / 40

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

30 / 40

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

31 / 40

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

32 / 40

________ networks are effective for learning embeddings from text data.

33 / 40

The term "padding" in CNNs refers to ________.

34 / 40

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

35 / 40

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

36 / 40

________ networks are effective for processing time series data.

37 / 40

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

38 / 40

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

39 / 40

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

40 / 40

________ networks are designed to handle dependencies between variables.

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