Explain weight initialization methods and goals

Q: Explain weight initialization methods and goals

This question evaluates a candidate's understanding of weight initialization in deep neural networks, assessing competencies in training dynamics such as symmetry breaking and the mitigation of vanishing or exploding activations and gradients.

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Explain why weight initialization matters in deep neural networks.

Then describe common initialization methods (such as random normal/uniform, Xavier/Glorot, and He initialization):

How each method chooses the initial weight distribution.
What problem(s) they are designed to solve (e.g., vanishing/exploding activations or gradients, symmetry breaking).
When you would choose each method based on the activation functions used.

Explain weight initialization methods and goals

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Explain weight initialization methods and goals

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