👉 Loop computing is a method used in machine learning and signal processing where a portion of the output from a computation loop is fed back into the input, creating a continuous cycle. This feedback mechanism allows the model to iteratively refine its predictions or estimates by incorporating previously computed results, effectively reducing errors and improving accuracy. Unlike traditional feedforward neural networks, loop computing maintains a dynamic, recurrent structure that enables the model to learn from its own past outputs, making it particularly useful for tasks involving time-series data or sequential decision-making processes. By continuously updating the model's internal state with new information, loop computing can adapt to changing patterns and dependencies within the data more effectively.
