Core Concepts of JAX

Mastering the core concepts of JAX is important for anyone looking to use its full capabilities. This chapter will talk about the fundamental principles that make JAX a strong tool for numerical computing and machine learning. You'll start with automatic differentiation, a needed feature that simplifies the calculation of derivatives, enabling efficient optimization in complex models.

We will also look into JAX's vectorization capabilities, allowing you to write code that operates on entire arrays, thereby improving performance and reducing the need for explicit loops. Another significant aspect we'll investigate is just-in-time compilation, which enhances execution speed by converting Python functions into optimized machine code. By the end of this chapter, you'll have a solid understanding of these important concepts, preparing you to implement them in practical scenarios.