Simple Linear Regression Example - Jax Typing CST Test
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Simple Linear Regression Example — Jax Code
A minimal JAX example performing linear regression using automatic differentiation.
import jax.numpy as jnp
from jax import grad, jit
# Sample data
x = jnp.array([1,2,3,4])
y = jnp.array([2,4,6,8])
# Initialize parameters
a = 0.0
b = 0.0
# Define loss function
def loss(a, b):
y_pred = a * x + b
return jnp.mean((y - y_pred)**2)
# Compute gradients
grad_loss = grad(loss, argnums=(0,1))
# Simple gradient descent loop
for _ in range(1000):
da, db = grad_loss(a, b)
a -= 0.01 * da
b -= 0.01 * db
print('Learned parameters:', a, b)Jax Language Guide
JAX is an open-source Python library for high-performance numerical computing, combining NumPy-like API with automatic differentiation (autograd), GPU/TPU acceleration, and composable function transformations for machine learning and scientific computing.
Primary Use Cases
- ▸High-performance machine learning and deep learning model development
- ▸Gradient-based optimization and automatic differentiation
- ▸Physics simulations and scientific computing requiring differentiable functions
- ▸Research in reinforcement learning and generative models
- ▸GPU/TPU accelerated numerical computing at scale
Notable Features
- ▸NumPy-compatible API with hardware acceleration
- ▸Automatic differentiation with `grad`
- ▸Just-in-time compilation (`jit`) for optimized performance
- ▸Vectorization (`vmap`) and parallelization (`pmap`) of functions
- ▸Composable transformations for advanced research pipelines
Origin & Creator
JAX was developed by researchers at Google Research starting in 2018, building on Autograd and XLA (Accelerated Linear Algebra) to enable high-performance, differentiable programming.
Industrial Note
JAX is widely used in cutting-edge machine learning research, physics simulations, reinforcement learning, and differentiable programming, where composable gradients and hardware acceleration are essential.