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An autoencoder is a type of neural network that encodes an input into a hidden representation and then decodes the hidden representation to reconstruct the input. If an undercomplete autoencoder (where the hidden dimension is less than the input dimension) can perfectly reconstruct the input from the hidden representation, it means the hidden representation captures all the important characteristics of the input. This is analogous to principal component analysis. The document then discusses regularization techniques for autoencoders like denoising, sparse, and contractive autoencoders.
Detailed Information
| Author: | ['vicky.sajnani'] |
|---|---|
| Publication Year: | 2020 |
| Pages: | 228 |
| Language: | English |
| Format: | |
| Price: | FREE |
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