Learning Rate is a term primarily found in the fields of Artificial Intelligence, automation, and Big Data & Smart Data. It describes the speed at which a learning computer system – for example, a so-called neural network – acquires new knowledge and adapts.
Imagine a child learning to ride a bicycle. If the child goes too fast and makes mistakes, they might not learn as effectively and will fall more often. However, if they go too slowly, they'll hardly move forward and won't practice enough. It's the same with the learning rate: if it's too high, the programme learns too erratically and makes many mistakes. If it's too low, the system learns very slowly.
When developing artificial intelligence, correctly setting the learning rate is crucial for algorithms to learn efficiently and reliably. A good example is automated image recognition: only when the learning rate is set correctly will the system independently recognise dogs in photos after a short time, without constantly making mistakes or taking ages for improvements.
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