The term \u201eLearning Representation\u201c is primarily found in the fields of Artificial Intelligence, Big Data and Smart Data, as well as automation. It refers to how a machine \u2013 for example, a computer program \u2013 represents data in a way that allows it to learn from it. The machine attempts to independently recognise important patterns and connections within the data.<\/p>\n
Imagine you want to teach a computer to distinguish between different fruits using images. \u201eLearning Representation\u201c ensures the programme recognises that a banana is curved and yellow, and an apple is round and red or green. The programme processes the images in such a way that these differences stand out clearly. <\/p>\n
The better this representation, the easier the system can \u201elearn\u201c \u2013 for example, to correctly assign new photos without having seen every image already. This is important because with Big Data, huge amounts of data need to be used meaningfully in a short time.<\/p>\n
Learning representations therefore plays a key role in gaining knowledge from data and making automated decisions. It forms the basis of many modern applications, such as in speech or image recognition, in process automation, or in online shopping recommendations.<\/p>","protected":false},"excerpt":{"rendered":"