How to speed up machine learning operations with Jax?

The machine learning algorithms require a lot of mathematical operations and as the performance of the model improves, its mathematical operations also increase with complexity. A simple example of this can be the random forest and decision tree where the random forest is more accurate in maximum cases but has complex mathematics and takes more time than the decision trees. Robust modelling requires a process where large mathematical or numerical operations can be completed robustly. Jax is a library that can help us in improving the speed of mathematical operations. In this article, we will discuss the Jax library in detail.