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small grammatical corrections, some formatting (#18345)

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nabilLearns
2018-10-12 21:56:54 -04:00
committed by Kristofer Koishigawa
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client/src/pages/guide/english/machine-learning/backpropagation/index.md
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@ -3,9 +3,14 @@ title: Backpropagation
---
## Backpropagation
Backprogapation is a subtopic of [neural networks](../neural-networks/index.md), and is the process by which you calculate 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.
Note: Backpropagation, and machine learning in general, required 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.
Backprogapation is a subtopic of [neural networks](../neural-networks/index.md).
**Purpose:** It is an algorithm/process with the aim of minimizing the cost function (in other words, the error) of parameters in a neural network.
**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.
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
@ -21,7 +26,7 @@ Given the following
- Delta a set of L matricies representing transitions between the ith and i+1th layer
- d(l) = the array of the gradients for layer l for one training example
- D a set of L matricies with the final gradients for each node
- lambda the regualrization term for the network
- lambda the regularization term for the network
In this case, for matrix M, M' will denote the transpose of matrix M
@ -40,9 +45,15 @@ In this case, for matrix M, M' will denote the transpose of matrix M
Of course, just seeing this article looks hugely complicated and should really only be understood in the greater contexts of neural networks and machine learning. Please look at the arrached references for a better understanding of the topic as a whole.
#### More Information:
* [Lecture 4 CS231n Introduction to Neural Networks](https://youtu.be/d14TUNcbn1k?t=354)
* [Siraj Raval - Backpropagation in 5 Minutes](https://www.youtube.com/watch?v=q555kfIFUCM)
* [Andrew Ng's ML Course](https://www.coursera.org/learn/machine-learning/)
* [In depth, wiki style article](https://brilliant.org/wiki/backpropagation/)
High-Level:
* Siraj Raval - [Backpropagation in 5 Minutes](https://www.youtube.com/watch?v=q555kfIFUCM)
* [Backprop on Wikipedia](https://en.wikipedia.org/wiki/Backpropagation)
In-depth:
* Lecture 4 CS231n [Introduction to Neural Networks](https://youtu.be/d14TUNcbn1k?t=354)
* [In depth, wiki style article](https://brilliant.org/wiki/backpropagation/)
* [A Step by Step Backpropagation Example](https://mattmazur.com/2015/03/17/a-step-by-step-backpropagation-example/)
* [Andrew Ng's ML Course](https://www.coursera.org/learn/machine-learning/)
If you'd like to learn how to implement a full-blown single (hidden) layer neural network in python, whilst learning more about the math behind the algorithms used, you can register for [Andrew Ng's Deep Learning Specialization] (https://www.coursera.org/specializations/deep-learning)