Loss Function

When predicting values from a model for the first time, you’re not sure whether your model will land on the actual predictions or not. As the weights are initialized randomly, it’s difficult for a model to predict the actual values. This is where our loss function is needed.

The loss function is an expression used to measure how close the predicted value is to the actual value. This expression outputs a value called loss, which tells us the performance of our model. By reducing this loss value in further training, the model can be optimized to output values that are closer to the actual values.

Loss functions are a crucial component in neural network training, as every machine learning model requires optimization, which helps in reducing the loss and making correct predictions. Without loss functions, there’s no way to drive your model to make correct predictions. But what exactly are loss functions, and how do you use them? In this article, we’ll look into the different loss functions available that can be used in the optimization of your models.