If we move small steps in the above example, we might end up with the same error, which is the case with discrete error functions. We apply small steps to minimize the error. In most real-life machine learning applications, we rarely make such a drastic move of the prediction line as we did above. To solve the error, we move the line to ensure all the positive and negative predictions are in the right area. You step towards the chosen direction, thereby decreasing the height, repeating the same process, always decreasing the height until you reach your goal = the bottom of the mountain. You will have to look at all possible directions and select a direction that makes you descend the most.

How do you choose the right direction to walk until you get to the bottom? Imagine you want to descend from the top of a big mountain on a cloudy day. WeвЂ™ll use two illustrations to understand continuous and discrete loss functions. рџ‘‰ PyTorch Loss Functions: The Ultimate GuideД¬ontinuous and discrete error/loss functions рџ‘‰ Keras Loss Functions: Everything You Need To Know In most cases, error function and loss function mean the same, but with a tiny difference.ДЄn error function measures/calculates how far our model deviates from correct prediction.ДЄ loss function operates on the error to quantify how bad it is to get an error of a particular size/direction, which is affected by the negative consequences that result in an incorrect prediction.ДЄ loss function can either be discrete or continuous. Applying cross-entropy in deep learning frameworks PyTorch and TensorFlow.(In binary classification and multi-class classification, understanding the cross-entropy formula) Difference between a discrete and a continuous loss function.

Cross entropy loss