What is the main disadvantage of using Attention in a neural network?

• It can be computationally expensive to implement

• It may not work well with all types of input data

• It may lead to overfitting of the network

• It requires a large amount of data for effective training

What is the primary disadvantage of using MLP compared to other neural network architectures?

• They are limited in their ability to process sequential data

• They are not suitable for image recognition tasks

• They require a large amount of data for effective training

• They are more computationally expensive compared to other neural networks

What is the difference between a shallow neural network and a deep neural network?

• A shallow neural network has only one hidden layer, while a deep neural network has multiple hidden layers

• A shallow neural network has more neurons in the input and output layers, while a deep neural network has more neurons in the hidden layers

• A shallow neural network is simpler and faster to train, while a deep neural network is more complex and slower to train

• None of the above

What is the purpose of activation functions in a neural network?

• To determine the output of each neuron in the network

• To introduce non-linearity into the network

• To regulate the flow of information in the network

• All of the above

What is the main advantage of using a neural network over other machine learning algorithms?

• They are capable of handling complex and non-linear relationships between input and output variables

• They are more suitable for image recognition tasks

• They are easier to train compared to other algorithms

• All of the above

What is the purpose of regularization in a neural network?

• To prevent overfitting of the network

• To improve the accuracy of the network

• To reduce the training time of the network

• To reduce the training time of the network

What is the difference between a convolutional neural network (CNN) and a recurrent neural network (RNN)?

• CNN is designed for image recognition tasks, while RNN is designed for sequential data processing

• CNN uses pooling layers, while RNN does not

• RNN is more computationally efficient compared to CNN

• CNN is more complex compared to RNN

What is the difference between a multilayer perceptron (MLP) and a deep neural network (DNN)?

• MLP has only one hidden layer, while DNN has multiple hidden layers

• MLP is simpler and faster to train compared to DNN

• DNN requires less data than MLP

• MLP is more computationally efficient compared to DNN

What is the difference between a feedforward neural network and a recurrent neural network?

• Feedforward neural network uses feedforward connections, while RNN uses recurrent connections

• Feedforward neural network is suitable for image recognition tasks, while RNN is suitable for sequential data processing

• RNN is more computationally efficient compared to feedforward neural network

• Feedforward neural network is more complex compared to RNN

What is the disadvantage of using linear activation function in a neural network?

• It can only handle linear relationships between input and output variables

• It can result in slow training time of the network

• It can lead to overfitting of the network

• It can reduce the accuracy of the network