Talk:Autoencoder

Robotics Stub‑class Mid‑importance

	This article is within the scope of WikiProject Robotics, a collaborative effort to improve the coverage of Robotics on Wikipedia. If you would like to participate, please visit the project page, where you can join the discussion and see a list of open tasks.RoboticsWikipedia:WikiProject RoboticsTemplate:WikiProject RoboticsRobotics
Stub	This article has been rated as Stub-class on Wikipedia's content assessment scale.
Mid	This article has been rated as Mid-importance on the project's importance scale.

Computer science Unassessed

This article is within the scope of WikiProject Computer science, a collaborative effort to improve the coverage of Computer science related articles on Wikipedia. If you would like to participate, please visit the project page, where you can join the discussion and see a list of open tasks.Computer scienceWikipedia:WikiProject Computer scienceTemplate:WikiProject Computer scienceComputer science

???

This article has not yet received a rating on Wikipedia's content assessment scale.

???

This article has not yet received a rating on the project's importance scale.

Things you can help WikiProject Computer science with:

Here are some tasks awaiting attention:

Article requests :
- Requested articles/Applied arts and sciences/Computer science, computing, and Internet
Cleanup :
- Computer science articles needing attention
- Computer science articles needing expert attention
Copyedit :
- Computing
Expand :
- Computer science
Infobox :
- Computer science articles without infoboxes
Maintain :
- Timeline of computing 2020–present
Photo :
- Find pictures for the biographies of computer scientists (see List of computer scientists)
- Computing articles needing images
Stubs :
- Computer science stubs
Unreferenced :
- WikiProject Computer science/Unreferenced BLPs
Project-related :
- Tag all relevant articles in Category:Computer science and sub-categories with {{WikiProject Computer science}}

Template:IEP assignment

Training section

Much of the training section does not seem to be directly related to auto-encoders in particular but about neural networks in general. No? Broken Segue 09:17, 29 August 2011 (UTC)[reply]

Clarification of "An output layer, where each neuron has the same meaning as in the input layer"

I don't understand what "has the same meaning as in the input layer" means in the output layer definition in the article. Can someone explain or clarify in the article, please. Many thanks, p.r.newman (talk) 09:53, 10 October 2012 (UTC)[reply]

==Answer: The outputs are the same as the inputs, i.e. y_i = x_i. The autoencoder tries to learn the identity function. Although it might seem that if the number of hidden units >= the number of input units (/output units) the resulting weights would be the trivial identity, in practice this does not turn out to be the case (probably due to the fact that the weights start so small). Sparse autoencoders, where a limited number of hidden layers can be activated at once, avoid this problem even in theory. 216.169.216.1 (talk) 16:47, 17 September 2013 (UTC) Dave Rimshnick[reply]