The term "convergence in training" originates from the fields of artificial intelligence, big data and automation. It is primarily used when a computer model - such as an artificial intelligence - becomes increasingly accurate and reliable through repeated training.
When an AI model is trained, it "learns" how to solve certain problems or make predictions based on many data patterns. The goal of this training is convergence. This means that after a certain amount of time and many training rounds, the model delivers stable, reliable results and no longer changes significantly. Only when the model has converged can it be used sensibly for practical tasks.
A simple example: Imagine a quality control system in a factory that is to be automated. Initially, the AI system makes a lot of mistakes because it is still learning when a product is faulty and when it is not. After many training runs with a wide variety of products, the system eventually recognises the "patterns" - it has achieved convergence in training and now works reliably and almost error-free.
Convergence in training is therefore a decisive step towards using automation, big data and artificial intelligence safely and effectively in companies.
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