👉 Feedback math is a method used in machine learning and deep learning to provide interpretable and actionable insights into the behavior of neural networks. It involves analyzing the gradients of the model's output with respect to its inputs, which helps identify which features or inputs are most influential in driving the model's predictions. By examining these gradients, one can understand which parts of the input data contribute most to the model's decisions, highlighting potential biases or errors in the model. Additionally, feedback math can reveal how changes in input data affect the model's output, aiding in model debugging and improvement. This approach is particularly valuable for enhancing transparency and trust in AI systems, especially in critical applications like healthcare or finance.
