Callbacks in Keras are used to execute code after every epoch True False.
Conv2D layer accepts three dimensional data True False.
Adversarial attacks change weights of an attacked network True False.
Convolutional Neural Networks (CNN) work on sequences of samples True False.
Recurrent Neural Networks (RNNs) are good at text processing tasks True False.
When Cohen's Kappa returns zero it means that the result is the same as random True False.
Accuracy measure is the number of correctly classified samples to the number of all samples True False.
Autoencoder is trained to return the same data as it was given on its input True False.
The MSE measure may be used instead of accuracy and typically gives better results True False.
When the model works very good for training data and it is poor for test data it means that we have a problem with:
overfitting mode collapse convergence failure .
The RELU activation function returns a value in range <0,1> True False.
Assuming that the list lst[] contains 100 elements, how many elements will be returned by the command: lst[1:4].
Generative Adversarial Networks (GANs) may be used to create images True False.
The SIGMOID activation function returns a value in range <0,1> True False.
The TANH activation function returns a value in range <-1,1> True False.
The output from the GAN generator network is one value True False.
Recurrent Neural Networks (RNNs) preserve state between processing samples True False.
Generative Adversarial Networks (GANs) consist of two competing networks True False.
Augmentation is the algorithm that changes the number of layers in a network True False.
Point the layers used in Recurrent Neural Networks: GRU LSTM UNET EDSR.
Which of these classes is NOT a layer in Keras/Tensorflow: Dense Conv2D BatchNormalization Numpy.
The Python package that handles multidimensional arrays is named: pandas numpy thorpy math.
When we use an existing network and retrain it with new samples it is called transfer learning True False.
Recurrent Neural Networks (RNNs) consist of two competing networks True False.
GradientTape is the specific network layer that calculates gradients True False.
Every neural network is convolutional True False.
LeakyRELU activation function returns a value in range <-infinity,infinity> True False.
Convolutional layers have typically less weights than dense layers True False.
Lower learning rate makes the model learn faster True False.
Convolutional Neural Networks (CNN) are very good at classification of images True False.
CycleGAN consists of four networks True False.
For the perfect model the confusion matrix has positive values only on its diagonal True False.
Recurrent Neural Networks (RNNs) work on sequences of samples True False.
Mark the loss function that may be used for regression tasks binary cross entropy mean absolute error categorical cross entropy.
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