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Is the vanishing gradient problem more likely to arise at the input or output layers of a neural network?

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Behavioral
  • Is the vanishing gradient problem more likely to arise at the input or output layers of a neural network?
  • In a neural network's architecture, does the vanishing gradient typically affect the initial layers or the final layers?
  • Where in a neural network is the vanishing gradient issue most prevalent, near the start or the end?
  • Does the vanishing gradient problem manifest nearer to the first or last layer of a neural network?
  • Can you tell if the vanishing gradient issue occurs at the start or closer to the completion of the neural network's layers?
  • In the structure of a neural network, are the early or later layers more affected by vanishing gradients?
  • Are the beginning layers or the ending layers of a neural network more susceptible to vanishing gradients?
  • Regarding neural networks, do vanishing gradients primarily impact the layers closer to the input or to the output?
  • In the layers of a neural network, where is the vanishing gradient issue more likely to be observed: closer to the beginning or towards the end?
  • In which part of a neural network does the vanishing gradient tend to be a bigger issue: the initial layers or the ones at the end?
  • Does the issue of vanishing gradient occur closer to the beginning or end of a neural network?

Interview question asked to Machine Learning Engineers interviewing at Affirm, Pinterest, Dropbox and others: Is the vanishing gradient problem more likely to arise at the input or output layers of a neural network?.