Neural Network Models MCQs With Answers

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

________ networks are designed to predict continuous outputs.

Dropout is a regularization technique used to prevent ________.

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

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

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

Residual connections in ResNet help address ________ during training.

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

The sigmoid activation function is used in ________ neural networks.

________ networks are designed to handle dependencies between variables.

________ networks are effective for learning embeddings from text data.

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

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

________ networks are effective for processing time series data.

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

________ are designed for processing sequences of data.

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

________ networks use reinforcement learning techniques to optimize actions.

The ResNet architecture introduces ________ connections to improve training.

Autoencoders are commonly used for ________ tasks.

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

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

________ are designed for image classification tasks.

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

Batch Normalization is used to ________ during neural network training.

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

Transfer learning in neural networks involves ________.

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

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

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

The term "padding" in CNNs refers to ________.

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

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

________ networks use attention mechanisms for focusing on important features.

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

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

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

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

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

The learning rate in neural networks controls ________.