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Can you describe the fundamental steps involved in the backpropagation process within neural networks?

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Anonymous

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backpropagation is a step in estimating the parameters of a model numerically using an optimization algorithm such as gradient decsent or adam. The optimization algorithm involves applying chain rule to the model,  it then makes a forward pass to calculate the gradient of the data for the calculated gradient, and then during the backpropagation, the optimizer updates the parameters of a model using the calculated gradient, a learning rate and the specific formulation of the optimizer. For Stochasting gradient decsent the delta is simply the learning rate multiplied by the gradient. 
  • Can you describe the fundamental steps involved in the backpropagation process within neural networks?
  • How would you outline the backpropagation algorithm in the context of neural networks?
  • Could you explain the essential phases of backpropagation used in neural network training?
  • What are the key steps in the backpropagation technique for neural networks?
  • Can you detail the sequential process of backpropagation in neural networks?
  • How does the backpropagation method function step-by-step in neural network optimization?
  • What constitutes the primary stages of backpropagation in a neural network's learning process?
  • Can you walk through the basic procedural steps of backpropagation in neural networks?
  • How would you summarize the basic actions taken during backpropagation in a neural network model?
  • Explain the basic steps of backpropagation in a neural network?
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Interview question asked to Machine Learning Engineers interviewing at Glovo, Blend, Microsoft and others: Can you describe the fundamental steps involved in the backpropagation process within neural networks?.