👉 Monitor math is a method of visualizing and understanding the relationship between the input and output of a neural network, particularly in deep learning models. It involves plotting the predicted outputs (the "monitor" values) alongside the actual target outputs to observe how well the model is performing at different stages of processing. By analyzing these plots, one can gain insights into various aspects of the model's behavior, such as whether it is learning meaningful features, if there are any biases or errors in its predictions, and how sensitive it is to input variations. This technique is especially useful for debugging and improving the model's performance, as it provides a clear visual representation of the model's internal processes and decision-making.
