Revert "vault backup: 2023-06-04 22:31:53"
This reverts commit e353f5fccb
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@ -44,13 +44,27 @@ $$\Delta w_{ji}(n)=\eta\delta_j(n)y_i(n)$$
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## Gradients
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#### Output Local
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$$\delta_j(n)=-\frac{\partial\mathfrak E (n)}{\partial v_j(n)}$$
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$$=-\frac{\partial\mathfrak E(n)}{\partial e_j(n)}\frac{\partial e_j(n)}{\partial y_j(n)}\frac{\partial y_j(n){\partial v_j(n)}$$
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$$=e_j(n)\cdot\varphi_j'(v_j(n))$$
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$$=-
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\frac{\partial\mathfrak E(n)}{\partial e_j(n)}
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\frac{\partial e_j(n)}{\partial y_j(n)}
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\frac{\partial y_j(n)}{\partial v_j(n)}$$
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$$=
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e_j(n)\cdot
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\varphi_j'(v_j(n))
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$$
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#### Hidden Local
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$$\delta_j(n)=-\frac{\partial\mathfrak E (n)}{\partial y_j(n)}\frac{\partial y_j(n)}{\partial v_j(n)}$$
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$$=-\frac{\partial\mathfrak E (n)}{\partial y_j(n)}\cdot\varphi_j'(v_j(n))$$
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$$\delta_j(n)=\varphi_j'(v_j(n))\cdot\sum_k \delta_k(n)\cdot w_{kj}(n)$$
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$$\delta_j(n)=-
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\frac{\partial\mathfrak E (n)}{\partial y_j(n)}
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\frac{\partial y_j(n)}{\partial v_j(n)}$$
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$$=-
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\frac{\partial\mathfrak E (n)}{\partial y_j(n)}
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\cdot
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\varphi_j'(v_j(n))$$
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$$\delta_j(n)=
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\varphi_j'(v_j(n))
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\cdot
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\sum_k \delta_k(n)\cdot w_{kj}(n)$$
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## Weight Correction
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$$\text{weight correction = learning rate $\cdot$ local gradient $\cdot$ input signal of neuron $j$}$$
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