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When considering neural network training, do vanishing gradients typically happen near the input layers or the output layers?

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Microsoft

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  • When considering neural network training, do vanishing gradients typically happen near the input layers or the output layers?
  • Within a neural network, are vanishing gradients more prone to occur near the beginning or the end of the network?
  • In the operational sequence of a neural network, is the vanishing gradient issue more common at the beginning or the end?
  • Regarding neural network layers, where does the vanishing gradient issue predominantly take place: at the start or the finish?
  • In neural networks, are the initial layers or the deeper layers more often affected by the problem of vanishing gradients?
  • For neural networks, is the vanishing gradient concern more frequent towards the network's input or its output?
  • Is the vanishing gradient phenomenon a more significant issue at the start or at the end of a neural network's layers?
  • At which point in a neural network do vanishing gradients most often present a problem: early on or closer to the output?
  • In the layout of a neural network, are the first layers or the final layers more at risk of experiencing vanishing gradients?
  • Does the phenomenon of vanishing gradients affect the initial stages or the later stages of a neural network's progression?
  • In the context of neural networks, does the issue of vanishing gradients occur closer to the start of the network or towards the end?
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Interview question asked to Machine Learning Engineers interviewing at Microsoft, Peek, SAP Concur and others: When considering neural network training, do vanishing gradients typically happen near the input layers or the output layers?.