Backpropagation Algorithm

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Backpropagation Algorithm
  • Algorihtm to minimize the cost function(BA) in particular

  • Need to compute the partial derrivative
  • Keep in mind that the hyphothesis is the row number

  • x,y only 1 training example, so just x and y
  • add a0 as the biased term
  • Next the partial derrivative will be calculated using Backpropagation algorithm

  • Each node for each layer will have error representation
  • delta will capture the error for every node
  • delta will be vector that has corresponding units with a and y
  • a3 in blue printed is the activation layer in layer 3
  • Backpropagation layer is propagating the error from last to first (reverse propagation)
  • Next use backpropagation to minimize cost function with lots of training set

  • triangle is capital delta used to compute the partial derrivative
  • error i not associated with input layer
  • Finally, the formula shown above will calculate the minimized cost function used for gradient descent or advanced optimization

  • So this is the backpropagation algorithm that used to calculate the partial derrivative cost function (Neural Networks)used in gradien descent and advanced optimization