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JAX for PyTorch Users
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Module 1
JAX Foundations and the Functional Mindset
1
JAX vs. NumPy vs. PyTorch: Execution, Immutability, and Purity
Explain how JAX differs from NumPy and PyTorch in terms of execution model, immutability, and functional purity.
2
JAX Arrays: NumPy & PyTorch Compared
Create, manipulate, and inspect jax.numpy arrays, contrasting them with NumPy arrays and PyTorch tensors.
3
Device Placement and Dtypes with jax.device_put
Use jax.device_put and inspect array device placement and dtypes on a single accelerator.
4
JAXifying Stateful NumPy with Pure Functions
Rewrite a stateful Python/NumPy routine into a pure function suitable for JAX transformations.
Reproducible Randomness with JAX Keys
Generate reproducible random numbers using jax.random keys, including key splitting and folding.
6
Migrating NumPy to JAX: Avoiding Common Pitfalls
Diagnose common pitfalls when porting NumPy code to JAX, such as in-place updates, Python control flow, and side effects.
7
Functional In-Place Array Updates with .at[].set/add
Use .at[].set/add for functional in-place array updates.
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Core Function Transformations: jit, grad, vmap