What is the relationship between the number of parameters in a neural network and its flexibility?
Deep Learning CNN Convolutional Neural Networks with Python - DropOut, Early Stopping and Hyperparameters

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1.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Flexibility is inversely proportional to the number of parameters.
Flexibility is directly proportional to the number of parameters.
Flexibility is unrelated to the number of parameters.
Flexibility decreases with more parameters.
2.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the primary purpose of using dropout in neural networks?
To ensure all nodes are always active.
To increase the number of parameters.
To enhance the model's accuracy on training data.
To prevent overfitting by reducing the number of active nodes.
3.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
How does dropout affect the neural network during training?
It increases the number of layers in the network.
It permanently removes nodes from the network.
It changes the activation function of the nodes.
It randomly deactivates nodes during each training iteration.
4.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the role of ReLU in neural networks?
It is used to initialize weights.
It increases the number of parameters.
It decreases the learning rate.
It acts as a regularization technique similar to dropout.
5.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Why is ReLU often used in combination with dropout?
To ensure all nodes are active.
To increase the number of layers.
To improve the model's performance and generalization.
To reduce the model's flexibility.
6.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the main goal of early stopping in neural network training?
To increase the training error.
To prevent overfitting by monitoring validation error.
To ensure the model overfits the training data.
To decrease the number of epochs.
7.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What does the patience parameter in early stopping control?
The dropout probability.
The initial learning rate.
The number of layers in the network.
The number of epochs to wait before stopping.
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