From 41f2f10e940519ca35f02a4920a04902a9b2a7a2 Mon Sep 17 00:00:00 2001 From: Ajay Arasanipalai Date: Sun, 14 Oct 2018 02:32:53 +0530 Subject: [PATCH] Add backpropogation and chain rule relation. (#18667) --- .../guide/english/machine-learning/backpropagation/index.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/client/src/pages/guide/english/machine-learning/backpropagation/index.md b/client/src/pages/guide/english/machine-learning/backpropagation/index.md index 807a5bbab2..94013db594 100644 --- a/client/src/pages/guide/english/machine-learning/backpropagation/index.md +++ b/client/src/pages/guide/english/machine-learning/backpropagation/index.md @@ -10,6 +10,8 @@ Backprogapation is a subtopic of [neural networks](../neural-networks/index.md). **Method:** This is done by calculating the gradients of each node in the network. These gradients measure the "error" each node contributes to the output layer, so in training a neural network, these gradients are minimized. +Backpropogation can be thought of as using the chain rule to compute gradients with respect to different parameters in a neural network in order to perform iterative updates to those parameters. + Note: Backpropagation, and machine learning in general, requires significant familiarity with linear algebra and matrix manipulation. Coursework or reading on this topic is highly recommended before trying to understand the contents of this article. ### Computation