Learn Machine Learning

556 readers

1 users here now

Welcome! This is a place for people to learn more about machine learning techniques, discuss applications and ask questions.

Example questions:

"Should I use a deep neural network for my audio classification task?"
"I'm working with a small dataset, what can I do to make my model generalize well?"
"Is there a library available that implements function X in language Y?"
"I want to learn more about the math behind machine learning technique A, where should I start?"

Please do:

Be kind to new people
Post guides and tutorials that you find helpful
Link to open/free sources instead of paywalled when possible

Please don't:

Post news articles / memes (there are other machine learning/AI communities for this)

Other communities in this area:

Similar subreddits: r/MLquestions, r/askmachinelearning, r/learnmachinelearning

founded 2 years ago

MODERATORS

ShadowAether

[email protected]

[Repost Q] Are autoencoders and auto-associative neural networks the same thing? (self.learnmachinelearning)

submitted 2 years ago* (last edited 2 years ago) by ShadowAether to c/learnmachinelearning

1 comments fedilink hide all child comments

This question is being reposted to preserve technical content removed from elsewhere. Feel free to add your own answers/discussion.

Original question: Autoencoders and auto-associative memory seem to be closely related. It appears the terminology changed, is there a difference between the two or did the wording simply change over time?

you are viewing a single comment's thread
view the rest of the comments

[–] ShadowAether 2 points 2 years ago

Original answer (source u/friendlykitten123):

Auto associative Neural networks are the types of neural networks whose input and output vectors are identical. These are special kinds of neural networks that are used to simulate and explore the associative process.

And Autoencoders are an unsupervised learning technique in which we leverage neural networks for the task of representation learning.

The difference being is that Autoencoders need at least 1 hidden layer to be constructed. But, Autoassociative neural networks could be obtained through a bidirectional associative memory, which is implemented with no hidden layer, and whose weights are learned in one step.

For more information, you can visit the following article:

https://ml-concepts.com/2022/03/10/everything-about-autoencoders/