Building and Training LLMs with JAX from Scratch

Building custom large language models requires an understanding of high-performance computing and hardware acceleration. This text-based course guides you through the foundational math, architecture, and programming paradigms needed to build and train your own neural networks from scratch. You will learn how to leverage JAX to scale your models efficiently and understand the engineering choices behind modern generative AI. By following this structured guide, you will transition from basic tensor operations to compiling and training a fully functional language model. What you'll learn: - Understand the core architecture of transformers and how attention mechanisms process text - Write clean JAX code using pure functions, functional programming paradigms, and immutable state - Apply JAX transformations like jit for compilation, grad for automatic differentiation, and vmap for vectorization - Configure and initialize model weights safely using modern initialization strategies - Train a custom language model using efficient optimization loops and loss functions - Practice debugging and profiling performance to prevent common gradient issues This course begins with essential theoretical concepts, tensor mathematics, and JAX fundamentals before moving step-by-step into model architecture design, training loop implementation, and optimization. You will work through written explanations and structured code blocks designed to build your practical intuition. This course is designed for software developers, data scientists, and engineers who want to understand the inner workings of language models. No prior experience with JAX is required, though a basic familiarity with Python and neural network concepts is recommended. Start reading today to master the underlying technology powering modern artificial intelligence.