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JAX Crash Course - Accelerating Machine Learning Code

AssemblyAI via YouTube

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Overview

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This crash course introduces JAX for high-performance numerical computing and machine learning research. It covers JAX's NumPy-compatible API, JIT compilation, automatic differentiation, vectorization, parallelization, functional-programming constraints, and a simple training loop.

Syllabus

Intro & Outline
What is JAX
Speed comparison
Drop-in Replacement for NumPy
jit: just-in-time compiler
Limitations of JIT
grad: Automatic Gradients
vmap: Automatic Vectorization
pmap: Automatic Parallelization
Example Training Loop
What’s the catch?

Taught by

AssemblyAI

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