JAX Explained: NumPy on GPUs with Automatic Differentiation
Learn how JAX brings NumPy to GPUs and TPUs with automatic differentiation and JIT compilation so your Python math runs faster without rewriting your code.
Numerical computing in Python should feel fast, simple, and easy to scale. That is exactly what JAX is built for.
JAX is a Python library that feels a lot like NumPy. You write the same kind of array code you already know. The difference is what happens behind the scenes. JAX is designed to run well on modern hardware like GPUs and TPUs. It also supports automatic differentiation and just in time compilation, without forcing you to rewrite your code from scratch.
If you have ever used NumPy and thought, this works great but I wish it were faster or easier to scale, JAX feels like the obvious next step. You keep writing familiar array based code, and JAX handles the heavy lifting. It moves work to accelerators and optimizes performance for you.
Welcome to Jax. Check out other 3 Random Articles here.
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What really makes JAX stand out is how cleanly it brings a few powerful ideas together. You get NumPy style APIs that feel natural. You get automatic differentiation, which makes working with gradients simple. And you get compilation through XLA, which can make your code much faster with very little extra work.
In this article, we are diving into JAX and looking at three practical features that show why so many people are moving past plain NumPy. No heavy theory. No vague explanations. Just clear examples that show how JAX works and when it makes sense to use it.
If you like clean math, fast code, and tools that can grow with you, this is worth checking out.
Let’s get it install via your terminal with pip:
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