Prerequisites: Familiarity with Python and NumPy is required. Basic understanding of machine learning concepts, such as arrays and gradients, is beneficial.

Level: Intermediate

What You'll Learn

• 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.