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