Timeline for Confusion in backpropagation algorithm
Current License: CC BY-SA 3.0
10 events
| when toggle format | what | by | license | comment | |
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| Aug 14, 2017 at 8:37 | history | edited | Neil Slater | CC BY-SA 3.0 |
Oops got sign of loss function wrong
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| Aug 12, 2017 at 23:04 | history | edited | Neil Slater | CC BY-SA 3.0 |
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| Aug 12, 2017 at 17:42 | vote | accept | lakshay taneja | ||
| Aug 12, 2017 at 17:42 | comment | added | lakshay taneja | I think i'm clear now in his video the way he shows diagrammatically it seems like the error is being propagated back but yeah its only true for the weights between hidden and output layer after that the recursive function he gave is using derivates from the previous layer.Its just that the way he explained it for the first layer got stuck in my mind anyways thanks for clearing my doubt :) | |
| Aug 12, 2017 at 17:27 | history | edited | Neil Slater | CC BY-SA 3.0 |
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| Aug 12, 2017 at 16:47 | history | edited | Neil Slater | CC BY-SA 3.0 |
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| Aug 12, 2017 at 15:44 | history | edited | Neil Slater | CC BY-SA 3.0 |
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| Aug 12, 2017 at 15:42 | comment | added | Neil Slater | @lakshaytaneja: There are no more neat tricks to shortcut the derivatives deeper in the network, but backpropagation is still all about gradients, there are no "variants" where it is about error values directly. I explained the output layer here, because that in my experience is a root cause for misunderstanding. Maybe explain more where your confusion is (why you think Andrew Ng is explaining distributing error values - he is not, but perhaps the explanation is missing something that would help you) by giving a specific quote? | |
| Aug 12, 2017 at 15:26 | comment | added | lakshay taneja | well i get that is this true for the weights between the hidden and the output layer but is it the same case between two hidden layers as the derivative of error at that node wont be y^ - y | |
| Aug 12, 2017 at 15:14 | history | answered | Neil Slater | CC BY-SA 3.0 |