Explain vanishing gradients and activations

Q: Explain vanishing gradients and activations

This question evaluates understanding of the vanishing gradient problem, high-level backpropagation dynamics, and the role of activation functions in gradient propagation, testing competency in neural network optimization and architecture.

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Question

Explain the vanishing gradient problem in deep neural networks.

In your answer:

Describe how backpropagation works at a high level and why gradients can vanish in deep networks.
Show how the choice of activation function (e.g., sigmoid, tanh, ReLU) affects gradient magnitude.
Discuss common techniques (including activation choices) to mitigate vanishing gradients.

Explain vanishing gradients and activations

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Explain vanishing gradients and activations

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