Adaptive learning refers to a type of machine learning that focuses on continuously adapting and adjusting to the input data as new data is obtained. Unlike static learning, where a machine learning model is trained once and used statically, adaptive learning allows the model to adapt and adjust as more data is collected.
In adaptive learning, the model is continuously trained with new data and uses feedback to update its parameters and adjust its behaviour. This allows the model to adapt to changing environmental conditions and improve its accuracy over time.
Adaptive learning is used in many applications, such as traffic prediction, energy demand forecasting and financial fraud detection. In these applications, the machine learning model must adapt to changing environmental conditions and continuously adjust to maintain its accuracy.
The use of Artificial Intelligence in business is becoming more and more common and necessary for the optimization and evolution of processes. In one of our [...]
Read More »What is Digital Transformation? The industrial revolution profoundly changed the society of the 19th century, but the digital transformation of the [...]
Read More »The term artificial intelligence (AI) is nowadays, but it was invented in 1956 by John McCarthy, Marvin Minsky and Claude Shannon in the famous [...]
Read More »Software as a Service (SaaS) companies have gained enormous prominence in the last few years, mainly due to the novelty of the products [...]
Read More »
