JAX
Learn JAX, a powerful numerical computation library with automatic differentiation and XLA compilation, ideal for high-performance machine learning research and custom model development.
5 Steps
- 1
Install JAX: Install JAX with CPU support using pip. For GPU or TPU support, follow the official JAX installation guide for specific instructions.
- 2
Basic JAX Operations: Explore basic JAX operations like array creation and manipulation, similar to NumPy.
- 3
Automatic Differentiation with JAX: Use `jax.grad` to automatically compute gradients of functions.
- 4
JIT Compilation with JAX: Use `jax.jit` to compile functions for faster execution using XLA.
- 5
Vectorization with `jax.vmap`: Use `jax.vmap` to automatically vectorize functions over array axes.
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