Artificial neural networks

An artificial neural network is based on a collection of nodes also known as artificial neurons, which loosely model the neurons in a biological brain. It is trained to recognise patterns; once trained, it can recognise those patterns in fresh data. There is an input, at least one hidden layer of nodes and an output. Each node applies a function and once the weight crosses its specified threshold, the data is transmitted to the next layer. A network is typically called a deep neural network if it has at least 2 hidden layers.

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Which algorithm is commonly used for optimizing neural networks?

Gradient Descent

K-Means

Support Vector Machines

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Which concept describes the architecture where neurons are organized into layers?

Feedforward structure

Sequential model

Layered topology

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What is overfitting in the context of neural networks?

When the model is too simple

When the model stops learning

When the model learns irrelevant details

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Which of these is a method used to initialize weights in a neural network?

Zero Initialization

Xavier Initialization

Random Search

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Which of the following is a type of recurrent neural network?

LSTM

RBF

KNN

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What is a common problem when training deep neural networks?

Excessive memory

Too few parameters

Vanishing gradients

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Which of the following best describes dropout in neural networks?

A type of activation function

A method to prevent overfitting

A way to reduce model complexity

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What is the purpose of a learning rate in neural networks?

To determine input size

To control weight updates

To adjust output size

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What is a convolutional layer primarily used for?

Feature extraction

Data storage

Data encryption

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What does LSTM stand for in neural networks?

Linear Sequence Time Model

Long Short-Term Memory

Large Scale Training Model

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Which type of neural network is typically used for image recognition?

Convolutional Neural Network

Recurrent Neural Network

Feedforward Neural Network

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What is a neuron in an artificial neural network?

A data storage unit

A processing unit

A data transmission unit

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What does the activation function in a neuron do?

It sums inputs

It transforms the input

It provides feedback

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What is the primary purpose of a pooling layer in a convolutional neural network?

To downsample feature maps

To increase dimensionality

To aggregate data

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Which component of a neural network determines the weighted sum of inputs?

Neuron

Activation function

Bias

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Which layer in a neural network is responsible for outputting the prediction?

Input layer

Hidden layer

Output layer

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What is the function of the optimizer in a neural network?

To compile the model

To visualize the data

To minimize the loss function

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What is backpropagation used for?

Input scaling

Data initialization

Training the network

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Which of the following is a common activation function?

Sigmoid

Linear

Cubic

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What is the primary function of an artificial neural network?

Data storage

Spatial data computation

Pattern recognition

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What is the primary function of an artificial neural network?

Pattern recognition

What is a neuron in an artificial neural network?

A processing unit

Which component of a neural network determines the weighted sum of inputs?

Neuron

What does the activation function in a neuron do?

It transforms the input

Which of the following is a common activation function?

Sigmoid

What is backpropagation used for?

Training the network

What is the purpose of a learning rate in neural networks?

To control weight updates

Which type of neural network is typically used for image recognition?

Convolutional Neural Network

What is overfitting in the context of neural networks?

When the model learns irrelevant details

Which of the following best describes dropout in neural networks?

A method to prevent overfitting

What is a convolutional layer primarily used for?

Feature extraction

Which algorithm is commonly used for optimizing neural networks?

Gradient Descent

Which of the following is a type of recurrent neural network?

LSTM

What does LSTM stand for in neural networks?

Long Short-Term Memory

What is a common problem when training deep neural networks?

Vanishing gradients

Which layer in a neural network is responsible for outputting the prediction?

Output layer

What is the primary purpose of a pooling layer in a convolutional neural network?

To downsample feature maps

Which of these is a method used to initialize weights in a neural network?

Xavier Initialization

What is the function of the optimizer in a neural network?

To minimize the loss function

Which concept describes the architecture where neurons are organized into layers?

Feedforward structure

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