The term autoencoder is particularly at home in the fields of artificial intelligence, big data and smart data as well as Industry and Factory 4.0.

An autoencoder is a special type of computer program that recognises patterns and correlations in large amounts of data. It helps to compress a lot of information so that only the most important information remains. An autoencoder works in a similar way to a copier with built-in error detection: it attempts to „learn“ input data - for example images or measured values - and then recreate them as accurately as possible.

Imagine you have thousands of photos of machines in a factory. With an autoencoder, the software can recognise the most important features of these photos and remove unimportant details. This makes it possible to save the images efficiently or to help detect unusual machine faults if a photo suddenly looks different from the patterns you have learnt.

In short: autoencoders help companies to better understand large amounts of data, recognise problems at an early stage and save storage space - and are therefore an important component of modern, intelligent data analysis.

The term autoencoder is particularly at home in the fields of artificial intelligence, big data and smart data as well as Industry and Factory 4.0.

An autoencoder is a special type of computer program that recognises patterns and correlations in large amounts of data. It helps to compress a lot of information so that only the most important information remains. An autoencoder works in a similar way to a copier with built-in error detection: it attempts to „learn“ input data - for example images or measured values - and then recreate them as accurately as possible.

Imagine you have thousands of photos of machines in a factory. With an autoencoder, the software can recognise the most important features of these photos and remove unimportant details. This makes it possible to save the images efficiently or to help detect unusual machine faults if a photo suddenly looks different from the patterns you have learnt.

In short: autoencoders help companies to better understand large amounts of data, recognise problems at an early stage and save storage space - and are therefore an important component of modern, intelligent data analysis.