What issue arises when a neural network gives too much importance to a single node?
Deep Learning - Deep Neural Network for Beginners Using Python - Dropout

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1.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
The model becomes less balanced.
The model becomes more accurate.
The model trains faster.
The model requires more data.
2.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the primary purpose of using dropout in neural networks?
To increase the number of nodes.
To ensure all nodes are equally important.
To simplify the model architecture.
To reduce the training time.
3.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
How does dropout affect the nodes during the training process?
It duplicates nodes.
It changes the node types.
It permanently removes nodes.
It randomly deactivates nodes.
4.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
In the context of dropout, what does a dropout rate of 0.2 signify?
20% of nodes are changed to output nodes.
20% of nodes are duplicated.
20% of nodes are permanently removed.
20% of nodes are deactivated at each layer.
5.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Which libraries are mentioned as having built-in support for dropout?
NumPy and SciPy
PyTorch and TensorFlow
Keras and Scikit-learn
Pandas and Matplotlib
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