APX AI
Online
Learn JAX for high-performance numerical computation and machine learning research. This course covers JAX fundamentals, including its NumPy API, function transformations like jit, grad, vmap, and pmap, and functional programming patterns for managing state. Gain practical experience accelerating and differentiating Python code for modern hardware (GPUs/TPUs).
Prerequisites Python and NumPy proficiency
Level:
Certification Available:
JAX Fundamentals
Understand the core concepts of JAX, its relationship with NumPy, and its functional programming approach.
Function Transformations
Apply JAX's key transformations: jit for compilation, grad for automatic differentiation, vmap for vectorization, and pmap for parallelization.
High-Performance Code
Write JAX code that effectively utilizes modern accelerators like GPUs and TPUs.
Automatic Differentiation
Compute gradients of Python functions automatically using grad.
State Management
Implement stateful computations using functional programming patterns suitable for JAX.
Debugging and Profiling
Identify common pitfalls and basic techniques for debugging JAX code.