Stateless Random Number Generation in JAX

Generating random numbers in parallel machine learning workflows often leads to unexpected bugs and non-reproducible results. Traditional stateful random number generators fail when scaled across multiple accelerator devices or parallelized transformations. This course teaches you how to leverage JAX's unique stateless pseudo-random number generation (PRNG) system to write predictable, reproducible, and highly parallelizable code. By completing this text-only course, you will understand the underlying mechanics of JAX's PRNG design and confidently implement reproducible random sampling in your own numerical computing pipelines. What you'll learn: - Understand the core differences between stateful and stateless pseudo-random number generation - Split and fold PRNG keys to safely generate independent random numbers across parallel processes - Implement reproducible stochastic operations in custom machine learning layers and functions - Integrate modern JAX transformations like jit, vmap, and pmap with stateless random operations - Debug common random seeding pitfalls in distributed training and simulation environments This course begins with foundational concepts of random number generation and state management before moving into practical JAX code structures and advanced parallelization patterns. Through clear explanations and structured code reading exercises, you will build a solid intuition for functional programming constraints and random state design. This course is designed for machine learning researchers, data scientists, and scientific computing developers who are new to JAX or looking to master its functional programming paradigm. No prior experience with JAX is required, though basic familiarity with Python and NumPy is recommended. Start writing robust, parallelizable, and reproducible stochastic code in JAX today.