Practice Questions
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How can the vanishing/exploding gradients problem lead to overfitting in business analytics models?
When gradients vanish, the model learns very slowly and only remembers tiny changes in the training data. Other options are incorrect because Gradient...
In the context of training deep neural networks, how does proper weight initialization help mitigate the vanishing/exploding gradients problem during backpropagation?
When weights are chosen with the right size, each layer passes gradients that stay about the same size. Other options are incorrect because People thi...
Which factor in backpropagation is significantly affected by the choice of activation functions, leading to issues like vanishing or exploding gradients?
The derivative of an activation function tells how much the output changes when the input changes. Other options are incorrect because Learning rate c...
In the context of training deep neural networks, which of the following scenarios best illustrates the impact of the vanishing/exploding gradients problem on backpropagation, training stability, and the risk of overfitting?
When gradients explode, the numbers that guide weight updates become huge. Other options are incorrect because Slow learning is not caused by explodin...
In the context of deep learning architectures, how can proper weight initialization and gradient clipping address the vanishing/exploding gradients problem effectively?
Randomly initializing weights with a uniform distribution keeps the size of signals the same as they move through layers. Other options are incorrect ...
Which of the following scenarios best exemplifies the vanishing/exploding gradients problem in neural networks?
When a network has many layers, the small changes that happen in the early layers get multiplied many times. Other options are incorrect because The i...
Which of the following techniques can help mitigate the vanishing or exploding gradients problem in deep neural networks? Select all that apply.
ReLU lets gradients flow because it does not shrink them. Other options are incorrect because Starting with very small weights makes the signal too we...
Vanishing gradients : shallow networks :: exploding gradients : ?
When a network has many layers, the gradient can become very small or very large as it moves backward. Other options are incorrect because A wider net...
In the context of training deep neural networks, the __________ problem refers to the difficulty in achieving convergence due to excessively small or large gradients during the optimization process.
When a network learns, it adjusts weights by following gradients. Other options are incorrect because Overfitting means the model memorizes training d...
A team of researchers is developing a deep neural network for image recognition, but they notice that the network struggles to learn effectively as they increase the number of layers. Which of the following strategies would best address the vanishing/exploding gradients problem they are facing?
Batch normalization normalizes the inputs to each layer, keeping values in a stable range. Other options are incorrect because Increasing the learning...
What is a primary cause of the vanishing gradients problem in deep neural networks?
When weights are set too small at the start, each layer multiplies the gradient by a number less than one. Other options are incorrect because Overfit...
Arrange the following steps in addressing the vanishing/exploding gradients problem in deep neural networks from first to last: A) Implement normalization techniques, B) Train the network, C) Initialize weights appropriately, D) Monitor gradient behavior during training.
First set the weights carefully. Other options are incorrect because This answer puts normalization before weight initialization; This answer starts b...
In the context of deep learning, which method is most effective in mitigating the vanishing/exploding gradients problem during training?
When a neural network starts training, the small numbers from each layer can shrink or grow too much. Other options are incorrect because A higher lea...
Why do deep neural networks suffer from the vanishing/exploding gradients problem during training?
When a gradient moves backward through many layers, it can shrink to almost nothing or grow to a huge number. Other options are incorrect because The ...
Master Vanishing/Exploding Gradients Problem
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