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Pytanie 1
Which of the following is a type of regularization that encourages weight values to be small but non-zero?
Dropout regularization
L1 regularization
None of the above
L2 regularization
Pytanie 2
Which of the following is a type of regularization that encourages sparse weight matrices?
L2 regularization
L1 regularization
Dropout regularization
None of the above
Pytanie 3
What is the purpose of early stopping as a regularization technique?
To minimize the training loss
To minimize the validation loss
To minimize the sum of training and validation loss
To prevent overfitting
Pytanie 4
Which of the following is a technique used for regularization in deep learning?
Dropout
Softmax
Stochastic Gradient Descent
Gradient Descent
Pytanie 5
Which of the following is a benefit of using multilayer perceptrons with multiple hidden layers?
They are less computationally expensive.
They require less labeled training data.
They are more easily interpretable.
They are less likely to overfit.
Pytanie 6
Which of the following is a disadvantage of using multilayer perceptrons?
They are computationally efficient.
They can suffer from the vanishing gradient problem.
They do not require labeled training data.
They are easy to interpret.
Pytanie 7
Which of the following is true about the backpropagation algorithm?
It is used to compute gradients of a loss function with respect to the weights of a neural network.
It is guaranteed to find the global minimum of the loss function.
It is only used for feedforward neural networks.
It does not require the use of an activation function.
Pytanie 8
Which of the following is not a method for avoiding overfitting in multilayer perceptrons?
Removing hidden layers
Dropout
Removing hidden layers
Regularization
Pytanie 9
Which of the following activation functions is not typically used in multilayer perceptrons?
Tanh
Softmax
ReLU
Sigmoid
Pytanie 10
What is the purpose of the bias term in a neural network?
To shift the activation function to the left or right
To introduce non-linearity into the network
To ensure that the output is always positive
To reduce the risk of overfitting
Pytanie 11
Which of the following is a common technique used to prevent overfitting in deep learning?
All of the above
Dropout
Early stopping
Data augmentation
Pytanie 12
What is the primary benefit of using mini-batches during training in deep learning?
Reduction of overfitting
Improved generalization to new data
All of the above
Faster convergence to a good solution
Pytanie 13
Which of the following is not a commonly used optimizer in deep learning?
Stochastic Gradient Descent (SGD)
Naive Bayes
Adam
RMSProp
Pytanie 14
What does the Perceptron Loss minimize?
The negative sum of the dot product between weights and inputs for all misclassified examples.
The sum of the absolute differences between predicted and target values.
The mean squared error between predicted and target values.
The entropy of the predicted probabilities compared to the true labels.
Pytanie 15
What does the Perceptron Loss minimize?
The number of misclassified examples by a perceptron.
The squared difference between the predicted output and the target output of a perceptron.
The number of iterations required for a perceptron to converge.
The average of the distances between the decision boundary and the training examples.
Pytanie 16
What is the main advantage of using convolutional neural networks for image recognition tasks?
They are more interpretable than other types of neural networks
They can handle variable-sized inputs
They require less training data than other types of neural networks
They can learn spatial hierarchies of features
Pytanie 17
Which of the following is not a common approach to unsupervised pretraining in deep learning?
Restricted Boltzmann Machines
Convolutional Neural Networks
Autoencoders
Deep Belief Networks
Pytanie 18
Which of the following is not a commonly used regularization technique in deep learning?
Random forest regularization
Dropout
L1 regularization
L2 regularization
Pytanie 19
What is the main problem with using the vanilla gradient descent algorithm for training deep neural networks?
It can lead to overfitting
It can be too slow to converge
It is computationally expensive
It can get stuck in local optima
Pytanie 20
Which of the following is not a commonly used activation function in deep learning?
ReLU
Tanh
Linear
Sigmoid
Pytanie 21
What is the purpose of the softmax function in deep learning?
To activate the neurons in the neural network
To compute the gradient of the loss function with respect to the weights
To calculate the output of the neural network
To normalize the output of the neural network to a probability distribution
Pytanie 22
What is the purpose of the backpropagation algorithm in deep learning?
To compute the gradient of the loss function with respect to the weights
To update the weights in the neural network
To calculate the output of the neural network
To propagate the input forward through the network
Pytanie 23
What is the difference between supervised and unsupervised learning?
There is no difference between the two
Supervised learning is more accurate than unsupervised learning
Supervised learning requires labeled data, while unsupervised learning does not
Supervised learning requires less training data than unsupervised learning
Pytanie 24
Which of the following is not a commonly used activation function in deep learning?
Tanh
Sigmoid
ReLU
Polynomial
Pytanie 25
What is the purpose of regularization in deep learning?
To prevent overfitting
To increase the accuracy of the model
To reduce the variance in the training data
To reduce the bias in the model
Pytanie 26
What is the purpose of using dropout in convolutional neural networks?
To reduce the computational cost of the model
To prevent overfitting in the model
To increase the number of parameters in the network
To increase the accuracy of the model
Pytanie 27
What is the purpose of using padding in convolutional neural networks?
To reduce the spatial dimensions of the input volume
To ensure that the output volume has the same spatial dimensions as the input volume
To reduce overfitting in the model
To increase the number of filters in the convolutional layer
Pytanie 28
Which of the following is used to reduce the spatial dimensions of the input volume in a convolutional neural network?
Fully connected layers
Activation functions
Convolutional layers
Pooling layers
Pytanie 29
What is the output shape of a convolutional layer with 32 filters, a filter size of 3x3, and input shape of 224x224x3?
222x222x32x3
222x222x32
222x222x3
32x32x3
Pytanie 30
Which of the following statements is true about convolutional neural networks?
Convolutional neural networks can only be used for image classification tasks.
Convolutional layers are always followed by fully connected layers.
The use of convolutional layers reduces the number of parameters in the network.
Convolutional neural networks cannot be used for object detection tasks.
Pytanie 31
Which of the following is a common technique used to prevent overfitting in convolutional neural networks?
Data augmentation
All of the above
Dropout
Early stopping
Pytanie 32
What is the main advantage of using convolutional layers in CNNs?
To increase the receptive field size of the network
To introduce nonlinearity into the network
To increase the number of learnable parameters
To reduce the spatial resolution of the input
Pytanie 33
Which of the following is a common activation function used in convolutional neural networks?
Sigmoid
Softmax
ReLU
Tanh
Pytanie 34
What is the main advantage of using pooling layers in CNNs?
To reduce the number of learnable parameters
To introduce nonlinearity into the network
To increase the spatial resolution of the feature maps
To increase the receptive field size of the network
Pytanie 35
Which of the following is a common problem in image classification that convolutional neural networks (CNNs) aim to address?
Underfitting on large datasets
Overfitting on small datasets
Slow training times
Lack of interpretability of the models
Pytanie 36
What is dropout regularization?
It adds the sum of squared values of the weights as a penalty term to the loss function.
It adds a Gaussian noise term to the weights during training.
It randomly removes a fraction of the neurons from the network during training.
It adds the sum of absolute values of the weights as a penalty term to the loss function.
Pytanie 37
What is L2 regularization?
It adds the sum of absolute values of the weights as a penalty term to the loss function.
It adds the sum of squared values of the weights as a penalty term to the loss function.
It adds the maximum squared value of the weights as a penalty term to the loss function.
It adds the maximum absolute value of the weights as a penalty term to the loss function.
Pytanie 38
What is the effect of increasing the regularization parameter in L2 regularization?
It reduces the magnitude of the weights.
It reduces the number of non-zero weights.
It has no effect on the weights.
It increases the magnitude of the weights.
Pytanie 39
What is weight decay?
It adds the sum of squared values of the w loss function.
It adds the sum of absolute values of the weights as a penalty term to the loss function.
It adds a Gaussian noise term to the weights during training.
It stops training the network when the validation error stops decreasing.
Pytanie 40
Which of the following is a technique used for data augmentation as a regularization technique?
None of the above
Subtracting noise from the input data
Removing a random subset of the input data
Adding noise to the input data
Pytanie 41
Which of the following is a step-size adjustment technique used in the stochastic gradient descent algorithm for optimization in deep learning?
Adagrad
Momentum
Learning rate decay
Nesterov momentum
Pytanie 42
Which of the following is a disadvantage of using the gradient descent algorithm for optimization in deep learning?
It does not require a learning rate
It is computationally expensive
It can only be used for convex objective functions
It may converge to a local minimum
Pytanie 43
What is the gradient descent algorithm used for optimization in deep learning?
An algorithm used to find the minimum value of the objective function
An algorithm used to compute the gradient of the objective function
An algorithm used to find the maximum value of the objective function
An algorithm used to find the stationary points of the objective function
Pytanie 44
What is the purpose of the DataLoader class in PyTorch?
To initialize the weights of a neural network model
To load the data in mini-batches during training
To define the computation graph of a neural network model
To preprocess the input data before training
Pytanie 45
Which of the following is true about the backward() method in PyTorch?
It is used to compute the forward pass of the model
It updates the model parameters using the computed gradients
It computes the gradients of the loss function with respect to the model parameters
It is used to perform inference on the trained model
Pytanie 46
What is the purpose of torch.nn.functional in PyTorch?
To initialize the weights of a neural network model
To compute the gradients of the loss function
To define the computation graph of a neural network model
To provide a set of pre-defined functions for neural network operations
Pytanie 47
Which of the following is used for creating custom datasets in PyTorch?
torch.nn
torch.utils.data
torch.optim
torch.utils.model
Pytanie 48
What is the PyTorch module used for optimizing neural network parameters?
torch.utils
torch.optim
torch.nn
torch.tensor
Pytanie 49
What is the PyTorch module used for building neural networks?
torch.nn
torch.tensor
torch.utils
torch.optim
Pytanie 50
Which of the following is not a PyTorch data type?
DoubleTensor
LongTensor
IntTensor
FloatTensor
Pytanie 51
What is the main difference between a traditional feedforward neural network and a recurrent neural network (RNN)?
Both RNNs and feedforward neural networks can process sequential data of varying lengths.
RNNs can process sequential data of varying lengths while feedforward neural networks cannot.
Feedforward neural networks can process sequential data of varying lengths while RNNs cannot.
RNNs are better suited for image classification tasks than feedforward neural networks.
Pytanie 52
What is the learning rate schedule used in Adam?
An exponentially decreasing learning rate
A constant learning rate
A linearly decreasing learning rate
A learning rate that adapts based on the history of the gradients
Pytanie 53
What is the role of the bias correction terms in Adam?
They help to reduce the variance of the updates
They correct for the fact that the moving averages start at zero
They increase the stability of the optimization process
They prevent the learning rate from getting too large
Pytanie 54
What is the update rule for the moving average of the gradient in Adam?
v_t = beta_2 * v_t-1 + (1 - beta_2) * g_t^2
m_t = beta_2 * m_t-1 + (1 - beta_2) * g_t
v_t = beta_1 * v_t-1 + (1 - beta_1) * g_t^2
m_t = beta_1 * m_t-1 + (1 - beta_1) * g_t
Pytanie 55
What is the key feature of Adam that distinguishes it from other optimization algorithms?
It scales the learning rate by the magnitude of the gradient
It computes an average of the past gradients for each parameter
It adapts the learning rate for each parameter
It uses momentum to smooth the parameter updates
Pytanie 56
Which of the following is an alternative to Adagrad that addresses its memory requirement issue?
RMSprop
Adam
Stochastic Gradient Descent
Adadelta
Pytanie 57
What is the main disadvantage of Adagrad?
It can be slow to converge
It requires a large amount of memory
It can lead to overfitting
It can get stuck in local optima
Pytanie 58
What is the advantage of using Adagrad over other optimization algorithms?
It is less prone to getting stuck in local optima
It is computationally efficient
It can adapt the learning rate for each parameter separately
It requires less memory
Pytanie 59
How does Adagrad adapt the learning rate for each parameter?
It updates the learning rate based on the gradient of the entire batch
It randomly selects a learning rate for each parameter during each iteration
It sets a fixed learning rate for all parameters
It uses a moving average of the past gradients for each parameter to scale the learning rate
Pytanie 60
What is Adagrad?
An optimization algorithm for training deep learning models
A regularization technique for reducing overfitting
A type of activation function used in neural networks
A loss function for measuring the difference between predicted and actual values
Pytanie 61
What is the purpose of the peephole connections in a peephole LSTM?
To allow the gates to adjust their activations based on the previous time step
To allow the gates to observe the input sequence directly
To allow the gates to observe the previous hidden state directly
To allow the gates to observe the current cell state directly
Pytanie 62
Which of the following is true about the output of an LSTM cell?
The output is always the same as the hidden state
The output is determined by the output gate
The output is determined by the forget gate
The output is a function of both the hidden state and the cell state
Pytanie 63
What is the difference between a standard LSTM cell and a peephole LSTM cell?
Peephole LSTM cells use a different activation function
Peephole LSTM cells do not have an output gate
Peephole LSTM cells have an extra forget gate
Peephole LSTM cells have additional connections from the cell state to the gates
Pytanie 64
Which of the following is an advantage of using LSTM over traditional recurrent neural networks (RNNs)?
LSTMs converge faster than RNNs
LSTMs are less prone to overfitting than RNNs
LSTMs can handle variable-length sequences
LSTMs require fewer parameters than RNNs
Pytanie 65
What is the purpose of the forget gate in an LSTM cell?
To determine the output of the LSTM cell
To determine the input to the output gate
To control how much of the cell state is updated
To decide whether to update the cell state or not
Pytanie 66
Which of the following is NOT a type of gate in an LSTM?
Output gate
Forget gate
Update gate
Input gate
Pytanie 67
What is the purpose of the teacher forcing technique in training RNNs?
To improve the generalization ability of the network.
To provide the network with the correct input at each time step during training.
To speed up the convergence of the network.
To prevent overfitting.
Pytanie 68
What is the difference between a unidirectional and bidirectional RNN?
A unidirectional RNN can only process data in one direction, while a bidirectional RNN can process data in both directions.
Both unidirectional and bidirectional RNNs can process data in both directions
A unidirectional RNN can process data in both directions, while a bidirectional RNN can only process data in one direction.
Both unidirectional and bidirectional RNNs can only process data in one direction.
Pytanie 69
What is the long short-term memory (LSTM) architecture designed to address in RNNs?
The underfitting problem.
The overfitting problem.
The vanishing gradient problem.
The exploding gradient problem.
Pytanie 70
What is the vanishing gradient problem in recurrent neural networks (RNNs)?
The weights of the network become too small.
The gradients become too small during backpropagation.
The gradients become too large during backpropagation.
The weights of the network become too large.
Pytanie 71
Which of the following is a potential application of UNet in medical image analysis?
Identifying objects in an image
Segmenting tumor regions in an MRI
Detecting anomalies in a time series
All of the above
Pytanie 72
How does UNet handle class imbalance in image segmentation tasks?
By undersampling the majority classes
UNet does not handle class imbalance
By weighting the loss function for underrepresented classes
By oversampling the minority classes
Pytanie 73
Which operation is used in UNet to recover the image resolution?
Pooling
Upsampling
Convolution
Dropout
Pytanie 74
Which part of UNet captures the context of an image?
All of the above
The contracting path
The expansive path
The bottleneck layer
Pytanie 75
What type of deep learning task is UNet commonly used for?
Natural language processing
Object detection
Image segmentation
Image classification
Pytanie 76
Which of the following is an application of autoencoders?
Image denoising
All of the above
Anomaly detection
Dimensionality reduction
Pytanie 77
What is the difference between a denoising autoencoder and a standard autoencoder?
Denoising autoencoders have an additional noise reduction layer.
Denoising autoencoders use noisy data as input during training.
Denoising autoencoders use a different activation function.
Denoising autoencoders use a different cost function.
Pytanie 78
What is the bottleneck layer in an autoencoder?
The last hidden layer in the encoder network.
The last hidden layer in the decoder network.
The first hidden layer in the encoder network.
The first hidden layer in the decoder network.
Pytanie 79
Which of the following is a type of regularized autoencoder?
Contractive autoencoder
All of the above
Denoising autoencoder
Sparse autoencoder
Pytanie 80
What is the objective of a variational autoencoder (VAE)?
To maximize the likelihood of the data under the encoder distribution.
To minimize the distance between the true data distribution and the learned distribution.
To maximize the lower bound on the log-likelihood of the data.
To minimize the reconstruction error between the input and the output.
Pytanie 81
Which of the following pooling methods is designed to capture spatial context in the feature maps?
Global pooling
Spatial pyramid pooling
Max pooling
Average pooling
Pytanie 82
What is the main disadvantage of using pooling layers in convolutional neural networks?
They can make the model more computationally expensive
They can increase the size of the output volume
They can lead to overfitting
They can reduce the representational capacity of the model
Pytanie 83
Which of the following statements about max pooling is true?
) It can be used as an alternative to fully connected layers
It can be used to learn translation invariance
It always produces a smaller output volume than the input volume
It performs the same operation on all feature maps in a given layer
Pytanie 84
Which of the following pooling methods does not involve any parameter learning?
Max pooling
L2 pooling
Global pooling
Average pooling
Pytanie 85
What is the purpose of pooling layers in convolutional neural networks?
To reduce the spatial dimensions of the output volume
To increase the number of parameters in the model
To add non-linearities to the model
To increase the size of the feature maps