Introduction to Loss Functions

Loss functions are at the core of training machine learning. They can be used to identify how well the model is performing on a dataset. Poor performance leads to a very high loss, while a well-performing model will have a lower loss. Therefore, the choice of a loss function is an important one when building machine learning models. In this article, we’ll look at the loss functions available in JAX and how we can use them.

Loss function

Machine learning models learn by evaluating predictions against true values and adjusting the weights. The objective is to obtain the weights that minimize the loss function. The loss function is also referred to as the cost function. The choice of a loss function depends on the problem. The two most common problems are classification and regression problems. Each will require a different set of loss functions.