Introduction to JAX

This chapter introduces JAX, a powerful library transforming numerical computing and machine learning. JAX excels by offering automatic differentiation, vectorization, and just-in-time compilation, enhancing performance and scalability in data science projects.

You will gain an understanding of JAX's unique features compared to libraries like NumPy or TensorFlow. We will cover fundamental concepts driving JAX's functionality, such as its grad function for differentiation and support for GPU and TPU acceleration.

By the end of this chapter, you will be able to:

1. Explain JAX's core principles and benefits.
2. Utilize JAX for basic numerical operations and understand its syntax.
3. Implement simple automatic differentiation in your code.

This chapter serves as your gateway to leveraging JAX for efficient and high-performance computing tasks. Practical examples will illustrate its capabilities, setting the stage for more advanced topics in subsequent chapters.