Backprogagation












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I am new to Deep Learning. Suppose that we have a neural network with one input layer, one output layer, and one hidden layer. Let's refer to the weights from input to hidden as w and the weights from hidden to output as v. Suppose that we have initialized w and v, and ran them through the neural network via the Feedforward algorithm. Suppose that we have calculated v via backprogagation. When estimating the ideal weights for w, do we keep the weights v constant when updating w via gradient descent given we already calculated v, or do we allow v to update along with w?



I understand that both w and v should update simultaneously when updating v, that's not my question. My question is related to if we need to update v when updating w, given we already calculated v.









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    I am new to Deep Learning. Suppose that we have a neural network with one input layer, one output layer, and one hidden layer. Let's refer to the weights from input to hidden as w and the weights from hidden to output as v. Suppose that we have initialized w and v, and ran them through the neural network via the Feedforward algorithm. Suppose that we have calculated v via backprogagation. When estimating the ideal weights for w, do we keep the weights v constant when updating w via gradient descent given we already calculated v, or do we allow v to update along with w?



    I understand that both w and v should update simultaneously when updating v, that's not my question. My question is related to if we need to update v when updating w, given we already calculated v.









    share







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    Joshua Jones is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
    Check out our Code of Conduct.







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      $begingroup$


      I am new to Deep Learning. Suppose that we have a neural network with one input layer, one output layer, and one hidden layer. Let's refer to the weights from input to hidden as w and the weights from hidden to output as v. Suppose that we have initialized w and v, and ran them through the neural network via the Feedforward algorithm. Suppose that we have calculated v via backprogagation. When estimating the ideal weights for w, do we keep the weights v constant when updating w via gradient descent given we already calculated v, or do we allow v to update along with w?



      I understand that both w and v should update simultaneously when updating v, that's not my question. My question is related to if we need to update v when updating w, given we already calculated v.









      share







      New contributor




      Joshua Jones is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.







      $endgroup$




      I am new to Deep Learning. Suppose that we have a neural network with one input layer, one output layer, and one hidden layer. Let's refer to the weights from input to hidden as w and the weights from hidden to output as v. Suppose that we have initialized w and v, and ran them through the neural network via the Feedforward algorithm. Suppose that we have calculated v via backprogagation. When estimating the ideal weights for w, do we keep the weights v constant when updating w via gradient descent given we already calculated v, or do we allow v to update along with w?



      I understand that both w and v should update simultaneously when updating v, that's not my question. My question is related to if we need to update v when updating w, given we already calculated v.







      neural-network deep-learning backpropagation





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      share







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      Joshua Jones is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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      asked 6 mins ago









      Joshua JonesJoshua Jones

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