JAX in 100 Seconds

JAX is a Python library for high-performance numerical computing, similar to Numpy but with added features like immutable arrays and pure functions. It supports accelerated hardware like GPUs and TPUs. JAX includes Autograph for automatic differentiation, crucial for machine learning tasks like optimization and backpropagation. It also features just-in-time compilation, transforming functions into primitive operations for efficient execution. JAX allows for high-performance array computing and automatic differentiation, enabling the development of advanced machine learning models.

What are the main features of JAX that differentiate it from Numpy?

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Explain the concept of automatic differentiation in JAX.

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How does JAX handle array mutability compared to Numpy?

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What does the 'A' in JAX stand for, and why is it important?

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Describe how JAX can be used to build deep neural networks.

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