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Getting Started with JAX Chapter 1: Introduction to JAX What is JAX?JAX vs. NumPy Setting Up JAX Chapter 2: Core Concepts of JAX JAX Arrays Function Transformations Random Number Generation Chapter 3: Automatic Differentiation in JAX Understanding Autograd Gradient Computation Higher-Order Derivatives Chapter 4: Vectorization with JAX JAX's vmap Function Batch Processing Parallelism in JAX Chapter 5: Just-in-Time Compilation Using JIT in JAX Performance Optimization Debugging JIT-Compiled Code Chapter 6: Practical Applications of JAX Machine Learning with JAX Scientific Computing Integrating JAX into Projects

Introduction to JAX

This chapter introduces JAX, a strong library changing numerical computing and machine learning. JAX stands out by offering automatic differentiation, vectorization, and just-in-time compilation, improving 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:

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

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

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