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Explaining Autoencoders seems to be a rite of passage for ML bloggers. It's conceptually understandable but has broad applications and is a jumping off point in
by SnooSux 4y ago
Explaining Autoencoders seems to be a rite of passage for ML bloggers. It's conceptually understandable but has broad applications and is a jumping off point into other architectures.
Educational content tends to have a bias towards concepts that are easier to explain rather than what is most useful. But as far as the latter goes, Autoencoders are a good place to start.
- uoaei 4y agoAutoencoders are insightful because they move the concept of "intermediate representations" to the foreground. This perspective is useful for understanding the compositional architectures of NNs at any scale. NNs can be generally described as "complicated feature engineering stacks followed by a relatively simple regression". This description captures the information processing and compression perspectives on NNs, which is realized by autoencoders in a laboratory-ideal way.