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