Transformers QUIZ (MCQ QUESTIONS AND ANSWERS)

Total Correct: 0

Time:20:00

Question: 1

Which type of neural network architecture is commonly used for generative tasks, such as image generation or text generation?

Convolutional Neural Network (CNN).

Recurrent Neural Network (RNN).

Autoencoder.

Feedforward Neural Network (FNN).

Question: 2

In the context of reinforcement learning, what is the "reward signal"?

A signal indicating the start of an episode.

A signal indicating the end of an episode.

A signal indicating the quality of an agent's action in an environment.

A signal indicating the learning rate.

Question: 3

What is the primary advantage of using a deep neural network with multiple hidden layers?

Reduced computational complexity.

Improved interpretability.

The ability to learn complex hierarchical features.

Faster training times.

Question: 4

What does the term "overfitting" refer to in the context of machine learning?

When a model performs well on the training data but poorly on unseen data.

When a model has too few parameters.

When a model performs equally well on both training and validation data.

When a model underfits the training data.

Question: 5

Which type of neural network layer is commonly used for image feature extraction?

Convolutional Layer.

Recurrent Layer.

Dense Layer.

Pooling Layer.

Question: 6

What is the purpose of data augmentation in deep learning?

To increase the size of the training dataset.

To reduce the learning rate.

To increase the number of hidden layers.

To preprocess input data.

Question: 7

What is a common activation function used in the output layer of a binary classification neural network?

ReLU.

Sigmoid.

Tanh.

Softmax.

Question: 8

Which optimization algorithm is commonly used for training deep neural networks?

Gradient Descent.

K-Means.

Principal Component Analysis (PCA).

Decision Trees.

Question: 9

What is the role of a loss function in training a neural network?

To increase model complexity.

To compute predictions.

To evaluate model performance.

To quantify the error between predicted values and ground truth.

Question: 10

What is the primary purpose of batch normalization in neural networks?

To increase the batch size during training.

To normalize input data.

To reduce computational complexity.

To stabilize and accelerate training by normalizing layer activations.

Question: 11

In neural network terminology, what is a "epoch"?

A type of activation function.

A type of layer.

One complete pass through the entire training dataset.

A type of regularization technique.

Question: 12

What is transfer learning in the context of deep learning?

The process of transferring model weights from one neural network to another.

The process of transferring knowledge learned from one task to improve performance on another task.

The process of transferring data between neural networks.

The process of fine-tuning a pre-trained model.

Question: 13

Which type of neural network architecture is well-suited for sequential data, such as time series or natural language?

Convolutional Neural Network (CNN).

Recurrent Neural Network (RNN).

Feedforward Neural Network (FNN).

Autoencoder.

Question: 14

What is the role of the softmax activation function in a classification neural network?

To introduce non-linearity into the model.

To reduce the dimensionality of the input data.

To compute class probabilities for multi-class classification.

To increase the number of layers in the network.

Question: 15

In the context of natural language processing, what is the purpose of tokenization?

To convert text into binary code.

To identify keywords in a text.

To split text into smaller units, such as words or subwords.

To translate text into multiple languages.

Question: 16

What is the primary purpose of transformers in deep learning?

Image classification.

Sequential data analysis.

Speech recognition.

Reinforcement learning.

Question: 17

What is the purpose of dropout regularization in neural networks?

To increase the number of neurons in each layer.

To reduce the dimensionality of the input data.

To prevent overfitting by randomly deactivating neurons during training.

To speed up training by skipping some training examples.

Question: 18

Question 14: What is the vanishing gradient problem in deep learning?

A problem where gradients become too large during training.

A problem where gradients become too small during training, hindering learning.

A problem where the learning rate is too high.

A problem where the model converges too quickly.

Question: 19

What is the purpose of activation functions in neural networks?

To reduce the dimensionality of data.

To preprocess input data.

To introduce non-linearity into the model.

To increase the number of layers in the network.

Question: 20

According to transformers' limitations, what might impact their use in certain scenarios?

Small number of parameters.

Lack of self-attention.

Computational expense and memory requirements.

Inability to handle sequential data.

Question: 21

What area of deep learning engineering is mentioned as a potential future application for transformers?

Developing low-end devices.

Large dataset analysis.

Computer vision.

Reinforcement learning.

Question: 22

Which transformer-based model was developed by Google and is popular for general question-answering tasks?

GPT-3.

BERT.

BART.

RoBERTa.

Question: 23

In the context of deep learning engineering, what limitation is associated with using transformers?

They are not effective for text processing.

They have a small number of parameters.

They are computationally expensive and require large datasets.

They are not suitable for natural language processing.

Question: 24

What is one of the best practices for optimizing transformer model performance?

Using a very small batch size.

Ignoring positional encoding.

Avoiding dropout layers.

Employing early stopping.

Question: 25

How is the transformer model trained to avoid overfitting?

Using dropout layers.

Reducing the learning rate.

Increasing the batch size.

Removing the positional encoding.

Question: 26

What type of tasks do transformers perform well in, especially in natural language processing?

Image classification.

Reinforcement learning.

Natural language processing tasks.

Numerical simulations.

Question: 27

Which part of the transformer architecture is responsible for adding positional information to the input sequence?

Self-Attention.

Multi-Head Attention.

Positional Encoding.

Layer Normalization.

Question: 28

What is the purpose of multi-head attention in the transformer architecture?

Increasing the number of layers.

Reducing computational complexity.

Focusing on different aspects of the input sequence.

Eliminating positional encoding.

Question: 29

Which advanced LSTM technique involves two LSTMs, one processing input in a forward trend and the other in a backward trend?

Stacked LSTM.

Bidirectional LSTM.

LSTM with attention mechanism.

Unidirectional LSTM.

Question: 30

In the context of transformers, what does self-attention refer to?

Paying attention to oneself.

Focusing on the most important words in a sentence.

Learning context in sequential data.

Avoiding overfitting.

Transformers QUIZ (MCQ QUESTIONS AND ANSWERS)

TIME:20:00

Time:20:00

Which type of neural network architecture is commonly used for generative tasks, such as image generation or text generation?

In the context of reinforcement learning, what is the "reward signal"?

What is the primary advantage of using a deep neural network with multiple hidden layers?

What does the term "overfitting" refer to in the context of machine learning?

Which type of neural network layer is commonly used for image feature extraction?

What is the purpose of data augmentation in deep learning?

What is a common activation function used in the output layer of a binary classification neural network?

Which optimization algorithm is commonly used for training deep neural networks?

What is the role of a loss function in training a neural network?

What is the primary purpose of batch normalization in neural networks?

In neural network terminology, what is a "epoch"?

What is transfer learning in the context of deep learning?

Which type of neural network architecture is well-suited for sequential data, such as time series or natural language?

What is the role of the softmax activation function in a classification neural network?

In the context of natural language processing, what is the purpose of tokenization?

What is the primary purpose of transformers in deep learning?

What is the purpose of dropout regularization in neural networks?

Question 14: What is the vanishing gradient problem in deep learning?

What is the purpose of activation functions in neural networks?

According to transformers' limitations, what might impact their use in certain scenarios?

What area of deep learning engineering is mentioned as a potential future application for transformers?

Which transformer-based model was developed by Google and is popular for general question-answering tasks?

In the context of deep learning engineering, what limitation is associated with using transformers?

What is one of the best practices for optimizing transformer model performance?

How is the transformer model trained to avoid overfitting?

What type of tasks do transformers perform well in, especially in natural language processing?

Which part of the transformer architecture is responsible for adding positional information to the input sequence?

What is the purpose of multi-head attention in the transformer architecture?

Which advanced LSTM technique involves two LSTMs, one processing input in a forward trend and the other in a backward trend?

In the context of transformers, what does self-attention refer to?