Summary
JAX is a Python- and NumPy-friendly library that offers high-performance tools that are specific to machine learning tasks. JAX centers its API around function transformations, allowing users, in one line of code, to pass in generic Python functions and receive transformed versions of the functions that would otherwise either be expensive to compute or require more advanced implementations. The syntax of function transformations also enables flexible and complex compositions of functions, which are common in machine learning.
Throughout this chapter, we have seen how to utilize JAX to compute the gradient of machine learning loss functions using automatic differentiation, JIT-compile our code for further optimization, and vectorize kernel functions via a binary classification example. However, these tasks are present in most use cases, and you will be able to seamlessly apply what we have discussed here to your own machine learning needs.
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