An autoencoder is a type of artificial neural network that is used to learn efficient representations of data. The main goal of an autoencoder is to reduce the dimensionality of the input data, i.e. compress it into a smaller feature space, and then reconstruct the original output data from this compressed representation.
An autoencoder consists of two main parts: the encoder and the decoder. The encoder takes the input data and transforms it into a compressed representation in feature space. The decoder takes this compressed representation and uses it to reconstruct the original output data.
The idea behind an autoencoder is that, by forcing the model to learn an efficient representation of the data, it is also forcing the model to identify the most important features of the input data. Therefore, autoencoders are useful for dimensionality reduction, data denoising and anomaly detection.
Once the basic concepts for building a commercial software with artificial intelligence are clear, where it is defined to whom to dedicate effort and [...]
Read More »You are probably wondering, what is surety insurance and how does it help your company? In today's economic environment, [...]
Read More »The term artificial intelligence (AI) is nowadays, but it was invented in 1956 by John McCarthy, Marvin Minsky and Claude Shannon in the famous [...]
Read More »To know how semantic technology works, the first thing you need to know is that it is responsible for helping artificial intelligence systems [...]
Read More »
