What is Loss?
In machine learning, loss is a measure of how well or poorly a model is performing on a given dataset. It represents the difference between expected result (Ground Truth) and the model response.
For example, if you're training a neural network to recognize images of cats, and the model incorrectly predicts a dog instead of a cat, the loss will be higher. The loss function helps to quantify how far off the model’s predictions are from the actual labels.
The goal of training a model is to minimize this loss by adjusting the model’s parameters (weights) so that it makes better predictions. Lower loss means the model is making more accurate predictions, while higher loss indicates more mistakes.
In machine learning, loss is a measure of how well or poorly a model is performing on a given dataset. It represents the difference between the predicted outputs (what the model predicts) and the actual outputs (the correct answers).