Notes on Backpropagation
@s i @s i @w ji (4) Examining each factor in turn, @E @y i = t i y i + 1 t i 1 y i; (5) = y i t i y i(1 y i); (6) @y i @s i = y i(1 y i) (7) @s i @w ji = h j (8) where x j is the activation of the j node in the hidden layer. Combining things back together, @E @s i = y i t i (9) and @E @w ji = (y i t i)h j (10). The above gives us the gradients ...
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