Assertion: neural network's layer | /r/HasProperty | ẟl
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Source sentences:
text: There are several calculations and errors that we need to compute whose formula are given by the backpropagation algorithm: 1) Output error (δL) related to the element wise (⦿) product of the gradient (▽C) by the derivative of activation function (σ′(z)), 2) error of one layer (ẟl) in terms of the error in the next layer related to the transpose matrix of the weights (Wl+1) multiplied by the error of the next layer (ẟl+1) and the element wise multiplication of the derivative of activation function, 3) rate of change of the cost with respect to any bias in the network: this means that the partial derivative of C with respect to any bias (∂C/∂bj) is equal to the error