👉 Returns computing is an advanced machine learning technique that aims to optimize the performance of neural networks by focusing on the most informative parts of the input data, rather than processing the entire dataset uniformly. This approach leverages the concept of sparse representations, where only a subset of features or neurons contributes significantly to the network's output. By identifying and amplifying these key components, returns computing can enhance model efficiency, reduce computational costs, and improve generalization performance, especially in scenarios with limited data or high-dimensional inputs. Essentially, it's a method to make neural networks more interpretable and effective by concentrating computational resources on the most relevant features.
