Long Short-Term Memory Networks QUIZ (MCQ QUESTIONS AND ANSWERS)

To perform image scaling.

To normalize the data between 0 and 1.

To create sequences for LSTM training.

To define the LSTM model architecture.

Memory Cell.

Input Gate.

Output Gate.

Forget Gate.

It leads to model overfitting.

It causes exploding gradients during training.

It hinders the convergence of the model.

It makes it difficult to update the network's weights effectively.

Image classification.

Handling sequential data and time series.

Natural language processing.

Structured data analysis.

Sigmoid

ReLU

Tanh

LeakyReLU

It leads to model overfitting.

It causes exploding gradients during training.

It hinders the convergence of the model.

It makes it difficult to update the network's weights effectively.

The model makes predictions on new data.

The model adjusts its weights using historical data.

The model generates sequences of data.

The model selects important features from the data.

Input Gate

Memory Cell

Output Gate

Forget Gate

To perform image scaling.

To normalize the data between 0 and 1.

To create sequences for LSTM training.

To define the LSTM model architecture.

To remember all information in the memory cell.

To update the memory cell with new data.

To prevent the LSTM from storing irrelevant or outdated information.

To control the output of the LSTM cell.

Memory Cell

Input Gate

Output Gate

Forget Gate

Overfitting

Lack of computational power

Vanishing gradient problem

Insufficient memory capacity

Image classification

Structured data

Sequential data and time series

Natural language processing

They are mainly used for image classification.

They are a type of feedforward neural network.

They are well-suited for sequential data and time series.

They do not have any internal memory.

Regulate cell state updates

Control the output of the unit

Determine the depth of the network

Compute the cell state

Handling image classification tasks.

Functioning as feedforward neural networks.

Excelling in sequential data and time series.

Lacking internal memory.

Regulate cell state updates

Control the output of the unit

Determine the depth of the network

Compute the cell state

Input gate

Forget gate

Output gate

Hidden gate

1

2

3

4

Forget gate

Input gate

Output gate

Cell state

Simplicity

Ability to handle vanishing gradients

Faster training

Lower computational requirements

Vanishing gradients

Exploding gradients

Overfitting

Lack of memory

Long Sequential-Term Memory

Limited Short-Term Memory

Long Short-Term Memory

Linear Sequential Memory

Stacked LSTM.

Bidirectional LSTM.

LSTM with attention mechanism.

Unidirectional LSTM.

Stacked LSTM.

Bidirectional LSTM.

LSTM with attention mechanism.

Unidirectional LSTM.

Faster training times.

Improved resistance to overfitting.

Better handling of images.

Effective modeling of long-term dependencies in sequential data.

To perform image scaling.

To normalize the data between 0 and 1.

To create sequences for LSTM training.

To define the LSTM model architecture.

To remember all information in the memory cell.

To update the memory cell with new data.

To prevent the LSTM from storing irrelevant or outdated information.

To control the output of the LSTM cell.

LSTM networks use more layers for better performance.

LSTM networks introduce memory cells and gates.

LSTM networks have larger hidden layers.

LSTM networks prioritize short-term dependencies.

Overfitting of data.

Vanishing gradient problem.

Lack of parallel processing capabilities.

Difficulty in handling structured data.