Introduction to JAX and Deep Learning

As deep learning systems grow in complexity, the tools we use to build them must support both performance and clarity. JAX has emerged as a powerful alternative to traditional frameworks, combining the simplicity of NumPy with the ability to scale across modern hardware. For engineers looking to learn JAX, the challenge is understanding how to think in a functional, composable way that aligns with modern deep learning workflows. I built this course from my experience working with neural networks and teaching advanced machine learning concepts across different levels of abstraction. A consistent gap I observed was that learners could use high-level frameworks, but struggled to understand what was happening under the hood. JAX provides that bridge, but only if it’s taught correctly. This course is designed to help you learn JAX not as a tool in isolation, but as a way of structuring deep learning systems more transparently. You’ll start with the foundations of JAX, including array operations, transformations, and automatic differentiation. From there, you’ll explore its ecosystem, Flax, Haiku, Optax, and more, while building intuition around randomness, optimization, and composable model design. Throughout the course, concepts from linear algebra and probability are applied directly to deep learning use cases, reinforcing both theory and practice. If you want to learn JAX in a way that deepens your understanding of deep learning while improving how you build models, this course provides a clear, structured path forward.