The term \u201eneural network visualisation\u201c is primarily found in the fields of Artificial Intelligence and Big Data. Neural networks are complex systems that enable machines to learn \u2013 for example, in recognising faces in photographs or translating languages. Because these networks often have millions of connections and parameters, it is difficult for humans to understand exactly how they work.<\/p>\n
Visualising neural networks means representing these complex processes in clear images or diagrams. This allows us to understand how the machine arrives at a decision. For developers and decision-makers, this makes it clear where the network is performing particularly well or poorly.<\/p>\n
A typical example: In a neural network that detects tumours on X-ray images, the visualisation highlights which areas of the image the network pays particular attention to, using colour. Doctors can thus immediately see whether the system is correctly assessing important features. Visualising neural networks thus helps to build trust in artificial intelligence and make its results more comprehensible.<\/p>","protected":false},"excerpt":{"rendered":"