INTERPRETING BY MEANS OF NEURAL NETWORKS: A GROUNDBREAKING CYCLE ENABLING RAPID AND UNIVERSAL COMPUTATIONAL INTELLIGENCE ARCHITECTURES

Machine learning has advanced considerably in recent years, with algorithms matching human capabilities in numerous tasks. However, the true difficulty lies not just in developing these models, but in deploying them efficiently in practical scenarios. This is where machine learning inference becomes crucial, emerging as a critical focus for experts