Partial derviative of prediction (sigmoid applied) with respect to weightGradient Descent Step for word2vec negative samplingImplementing RMSProp, but finding differences between reference versionsWhy is vanishing gradient a problem?SGD learning gets stuck when using a max pooling layer (but it works fine with just conv + fc)Why are optimization algorithms slower at critical points?Why Gradient methods work in finding the parameters in Neural Networks?Is gradient descent slower for finite differences?Creating a convolutional layer with weight normalization?
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Partial derviative of prediction (sigmoid applied) with respect to weight
Gradient Descent Step for word2vec negative samplingImplementing RMSProp, but finding differences between reference versionsWhy is vanishing gradient a problem?SGD learning gets stuck when using a max pooling layer (but it works fine with just conv + fc)Why are optimization algorithms slower at critical points?Why Gradient methods work in finding the parameters in Neural Networks?Is gradient descent slower for finite differences?Creating a convolutional layer with weight normalization?
$begingroup$
I am very confused as to where a seemingly "extra" term is included in the above mentioned calculation in my Udacity course.
The above is taking the derivative of a sigmoid so why isn't it just
$$=sigma(Wx+b)(1-sigma(Wx+b)$$
but rather has $fracpartialpartial w_j(Wx+b)$ tacked on the tail?
gradient-descent
New contributor
$endgroup$
add a comment |
$begingroup$
I am very confused as to where a seemingly "extra" term is included in the above mentioned calculation in my Udacity course.
The above is taking the derivative of a sigmoid so why isn't it just
$$=sigma(Wx+b)(1-sigma(Wx+b)$$
but rather has $fracpartialpartial w_j(Wx+b)$ tacked on the tail?
gradient-descent
New contributor
$endgroup$
add a comment |
$begingroup$
I am very confused as to where a seemingly "extra" term is included in the above mentioned calculation in my Udacity course.
The above is taking the derivative of a sigmoid so why isn't it just
$$=sigma(Wx+b)(1-sigma(Wx+b)$$
but rather has $fracpartialpartial w_j(Wx+b)$ tacked on the tail?
gradient-descent
New contributor
$endgroup$
I am very confused as to where a seemingly "extra" term is included in the above mentioned calculation in my Udacity course.
The above is taking the derivative of a sigmoid so why isn't it just
$$=sigma(Wx+b)(1-sigma(Wx+b)$$
but rather has $fracpartialpartial w_j(Wx+b)$ tacked on the tail?
gradient-descent
gradient-descent
New contributor
New contributor
edited yesterday
flexitarian33
New contributor
asked yesterday
flexitarian33flexitarian33
256
256
New contributor
New contributor
add a comment |
add a comment |
1 Answer
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$begingroup$
Recall that for chain rule, we have $$fracddwh(g(w))=h'(g(w))g'(w)$$
For the context of your question, $h(t)=sigma(t)$ and $g(w)=Wx+b$,
hence that is why we have one more term.
$endgroup$
$begingroup$
I can see now that it's a composite function since sigmoid itself is a function :)
$endgroup$
– flexitarian33
yesterday
add a comment |
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$begingroup$
Recall that for chain rule, we have $$fracddwh(g(w))=h'(g(w))g'(w)$$
For the context of your question, $h(t)=sigma(t)$ and $g(w)=Wx+b$,
hence that is why we have one more term.
$endgroup$
$begingroup$
I can see now that it's a composite function since sigmoid itself is a function :)
$endgroup$
– flexitarian33
yesterday
add a comment |
$begingroup$
Recall that for chain rule, we have $$fracddwh(g(w))=h'(g(w))g'(w)$$
For the context of your question, $h(t)=sigma(t)$ and $g(w)=Wx+b$,
hence that is why we have one more term.
$endgroup$
$begingroup$
I can see now that it's a composite function since sigmoid itself is a function :)
$endgroup$
– flexitarian33
yesterday
add a comment |
$begingroup$
Recall that for chain rule, we have $$fracddwh(g(w))=h'(g(w))g'(w)$$
For the context of your question, $h(t)=sigma(t)$ and $g(w)=Wx+b$,
hence that is why we have one more term.
$endgroup$
Recall that for chain rule, we have $$fracddwh(g(w))=h'(g(w))g'(w)$$
For the context of your question, $h(t)=sigma(t)$ and $g(w)=Wx+b$,
hence that is why we have one more term.
answered yesterday
Siong Thye GohSiong Thye Goh
1,302418
1,302418
$begingroup$
I can see now that it's a composite function since sigmoid itself is a function :)
$endgroup$
– flexitarian33
yesterday
add a comment |
$begingroup$
I can see now that it's a composite function since sigmoid itself is a function :)
$endgroup$
– flexitarian33
yesterday
$begingroup$
I can see now that it's a composite function since sigmoid itself is a function :)
$endgroup$
– flexitarian33
yesterday
$begingroup$
I can see now that it's a composite function since sigmoid itself is a function :)
$endgroup$
– flexitarian33
yesterday
add a comment |
flexitarian33 is a new contributor. Be nice, and check out our Code of Conduct.
flexitarian33 is a new contributor. Be nice, and check out our Code of Conduct.
flexitarian33 is a new contributor. Be nice, and check out our Code of Conduct.
flexitarian33 is a new contributor. Be nice, and check out our Code of Conduct.
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