Intro to Autoencoders
This tutorial introduces autoencoders with three examples: the basics, image denoising, and anomaly detection.
autoencoders anomaly-detection time-series denoising image-denoising computer-vision notebook code article tensorflow tutorial

This tutorial introduces autoencoders with three examples: the basics, image denoising, and anomaly detection.

An autoencoder is a special type of neural network that is trained to copy its input to its output. For example, given an image of a handwritten digit, an autoencoder first encodes the image into a lower dimensional latent representation, then decodes the latent representation back to an image. An autoencoder learns to compress the data while minimizing the reconstruction error.

To learn more about autoencoders, please consider reading chapter 14 from Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville.

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