Foundational JAX: Building Machine Learning Models from Scratch

High-performance machine learning requires tools that can scale seamlessly across modern hardware. JAX offers a powerful, NumPy-like interface with hardware acceleration and automatic differentiation built right in. This text-based course guides you through the foundational concepts of JAX, helping you transition from standard Python mathematical operations to writing highly optimized machine learning models. You will learn to leverage functional programming principles to write clean, reproducible, and blazing-fast code. What you'll learn: - Understand the core principles of JAX, including immutable arrays and functional programming paradigms. - Apply automatic differentiation using grad to compute gradients for complex mathematical functions. - Speed up your execution pipelines using Just-In-Time (JIT) compilation with jit. - Vectorize your computations efficiently using vmap to handle batches of data without manual loops. - Build neural networks from scratch and explore modern JAX-based libraries like Flax for structuring models. - Implement key machine learning algorithms, including linear regression and basic neural network layers, using accelerated workflows. The course begins with foundational concepts and JAX syntax before guiding you step-by-step through building, compiling, and optimizing machine learning models. Designed for beginners to JAX, this course requires only a basic understanding of Python and high-school level algebra, with no prior experience in advanced deep learning frameworks needed. Start reading today to unlock the power of high-performance machine learning with JAX.