front matter
preface
JAX is a powerful Python library created by Google for deep learning and high-performance computing. It’s widely used in machine learning research and ranks as the third most popular deep learning framework, trailing only behind TensorFlow and PyTorch. Notably, it’s the go-to framework for companies like DeepMind, and Google’s research increasingly relies on JAX.
What I really appreciate about JAX is its emphasis on functional programming in deep learning. It offers robust function transformations, including gradient computation, JIT compilation via XLA, auto-vectorization, and parallelization. JAX supports both GPUs and TPUs, delivering impressive performance.
Now is an exciting time to dive into JAX, as its ecosystem is rapidly expanding. Despite being around for a few years, there’s a noticeable lack of comprehensive resources for beginners. While JAX’s website offers solid documentation and a supportive community, piecing everything together, especially when integrating other libraries, can be daunting.
This book is crafted for those eager to master JAX. My goal is to consolidate crucial information in one place and guide you through understanding JAX concepts, enhancing your skills and ability to apply JAX in your projects and research.