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Updated source.txt to fix typo. #1

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Updated source.txt to fix typo. #1

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2 changes: 1 addition & 1 deletion Backprop/source.txt
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Expand Up @@ -100,7 +100,7 @@ $$ a_k^l = \sigma(z_k^l) $$

Now finally calculate the input sum of a neuron $m$ in layer $l+1$.

$$ z_m^{l+1} = \sum_{k} w_{mk}^{l+1} a^l_k + b_m^l $$
$$ z_m^{l+1} = \sum_{k} w_{mk}^{l+1} a^l_k + b_m^{l+1} $$

Here we have gone forward one step in the layers, from the activations in layer $l-1$ to the input sums of neurons in layer $l+1$. An error function C is defined using one example from our training data, and its derivative is calculated with respect to a single weight in layer $l$.

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