analiza aiqbench

This article focuses on the mathematical foundations of backpropagation, specifically addressing its computational inefficiencies and proposing alternative methods like gradient-free optimization or differentiable programming as more scalable solutions for large-scale models. By re-evaluating how gradients are calculated to reduce memory overhead, the piece offers critical insights into improving training efficiency without relying solely on standard deep learning frameworks.