Part 1. First steps

Embark on a journey into the world of JAX, a cutting-edge library that’s revolutionizing deep learning and high-performance computing. In this opening part of JAX for Deep Learning, we lay the groundwork for understanding why JAX is a pivotal tool in the ever-evolving landscape of machine learning frameworks. Through two foundational chapters, you’ll gain insights into the unique advantages of JAX over other popular libraries like TensorFlow, PyTorch, and NumPy and learn how to harness its power for your deep learning projects.

Chapter 1 introduces you to the core concepts and strengths of JAX. It sets the stage by delving into the history of deep learning frameworks, highlighting the evolutionary path that led to the birth of JAX. This chapter illuminates JAX’s niche in the ecosystem, showcasing its adeptness at compiling and parallelizing code for a wide range of applications, from ocean simulation to large-scale neural networks. By comparing JAX with TensorFlow, PyTorch, and NumPy, you’ll understand the strategic scenarios that call for JAX’s utilization, laying a robust foundation for the rest of the book.