reverse mode

Reverse mode, commonly used in machine learning, is a method for efficiently calculating how changes in input variables affect the output of a function, especially when dealing with many inputs. It works by first computing the output's value and then working backwards to determine each input's contribution to that output. This process, often called backpropagation in neural networks, allows for quick calculation of gradients, which are essential for optimizing models. Essentially, reverse mode helps us understand the influence of each input on the result without recalculating everything multiple times, improving efficiency in training complex systems.