Easy way to compute Jacobian and gradient with forward and back propagation in graph

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Before watching, you can refresh the notion of the differential with my other video:
Lecture 2-3: Gradient and Hessian of Multivariate Function

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  1. Michael Zibulevsky
  2. YouTube Editor
  3. neutal networks
  4. deep learning
  5. convolutional neural networks
  6. back-propagation
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Комментарии
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It's so clear and easy when you make the math visualized in graphs.
Thank you Michael - it's beautiful!

yacovhel-or
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This is the best general derivation I have seen so far connecting Jacobian and its relation to Gradient in NN. I am eager to see something similar with Hessian!

edwardwu
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Thank you very much Sir! Can you solve an example ANN back propagation using this. 1 input layer 5 neurons, 3 hidden layers 4 neurons, 1 output layer 2 neurons.

MrStudent
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more math and less words ! great. Thank you.

mostafaorooji
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Nice Explanation (y), , But Why we have the functional Matrix in Column vector form? What this collection of function in column matrix represent corresponds to neural network?

EhsanIrshad
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What is advantage of using Jacobian in convolution neual network?

neelamadhabkhaya
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Cute mathematics! Thanks for your clear explanation!

DavidKhosid