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About Autoencoders - Bits and Bytes of Deep Learning - Towards Data Science
This document discusses autoencoders, which are a type of neural network used for data compression and dimensionality reduction. Autoencoders compress the input into a latent-space representation and then reconstruct the output from this representation, making them useful for tasks like data denoising and dimensionality reduction for visualization. Convolutional autoencoders use the convolution operator to learn representations made up of simple signals. The document provides code for a simple autoencoder model in Keras that compresses MNIST digits to a lower dimensional encoding and then reconstructs the output.
Detailed Information
| Author: | ['Mohit Singh'] |
|---|---|
| Publication Year: | 2018 |
| Pages: | 10 |
| Language: | English |
| Format: | |
| Price: | FREE |
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