Deep Belief Networks (DBNs) are at home in the fields of Artificial Intelligence, Big Data and Smart Data, as well as Digital Transformation. They are among the modern methods with which computers and machines learn to recognise patterns and correlations in large amounts of data.

Imagine Deep Belief Networks as a network of several layers stacked on top of each other, much like the layers of a cake. Each layer is responsible for a specific detail, such as recognising colours, edges, or shapes in an image. The deeper the layers, the more complex the patterns that can be recognised.

A prime example: A deep belief network can teach a computer to reliably recognise handwritten numbers on a form – even if each person writes them slightly differently. To do this, the network analyses many different images of numbers, learns from these examples and ultimately recognises new, unknown numbers.

Deep Belief Networks (DBNs) are deployed wherever large amounts of data need to be processed and understood automatically, such as in image or speech recognition, or when analysing customer data.

Deep Belief Networks (DBNs) are at home in the fields of Artificial Intelligence, Big Data and Smart Data, as well as Digital Transformation. They are among the modern methods with which computers and machines learn to recognise patterns and correlations in large amounts of data.

Imagine Deep Belief Networks as a network of several layers stacked on top of each other, much like the layers of a cake. Each layer is responsible for a specific detail, such as recognising colours, edges, or shapes in an image. The deeper the layers, the more complex the patterns that can be recognised.

A prime example: A deep belief network can teach a computer to reliably recognise handwritten numbers on a form – even if each person writes them slightly differently. To do this, the network analyses many different images of numbers, learns from these examples and ultimately recognises new, unknown numbers.

Deep Belief Networks (DBNs) are deployed wherever large amounts of data need to be processed and understood automatically, such as in image or speech recognition, or when analysing customer data.