JAX : Content Page

Google JAX Series Contents

1. Introduction to Google JAX

• 1.1 Overview of JAX
• 1.2 Key Features and Benefits

2. Getting Started with JAX

• 2.1 Setting up the Environment
• 2.2 Basic Operations with JAX
• 2.3 Understanding JAX vs. NumPy Arrays

3. Automatic Differentiation with Autograd in JAX

• 3.1 Principles of automatic differentiation.
• 3.2 Implementing gradient descent from scratch.

4. Just-In-Time Compilation with jit

• 4.1 How jit works and its benefits.
• 4.2 Practical examples of speed improvements.

5. Vectorization in JAX with 'vmap' and 'pmap'

• 1.1 Understanding vectorization and parallelism.
• 1.2 Use cases and performance implications.

6. Linear Algebra Optimized: JAX and XLA

• 1.1 Introduction to XLA and how JAX utilizes it.
• 1.2 Benchmarks comparing JAX/XLA with other frameworks.

7. Deep Learning with JAX

• 1.1 Building neural networks from scratch.
• 1.2 Using libraries like Flax and Haiku for model building.

8. State and Side-Effects in JAX

• 1.1 Understanding pure functions.
• 1.2 Managing state with jax.lax.scan.

9. Advanced JAX: Custom Gradients and Jacobians

• 1.1 Creating custom gradient functions.
• 1.2 Computing Jacobians and Hessians.

10. Probabilistic Programming in JAX

• 1.1 Libraries like NumPyro for Bayesian modeling.
• 1.2 Building and inferring probabilistic models.

11. High-Performance Computing with JAX

• 1.1 Utilizing GPUs and TPUs.
• 1.2 Best practices for maximizing performance.

12. JAX in Research: Case Studies and Applications

• 1.1 Showcase real-world applications and research projects using JAX.

13. Debugging and Profiling JAX Applications

• 1.1 Tools and techniques for debugging.
• 1.2 Profiling applications and identifying performance bottlenecks.

14. Interoperability: Integrating JAX with Other Frameworks

• 1.1 Using JAX with TensorFlow, PyTorch, etc.
• 1.2 Converting models and sharing data between frameworks.

15. The Future of JAX

• 15.1 Upcoming Features and Directions
• 15.2 Community Contributions and How to Get Involved

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Introduction to Jax

Tags:

• #GoogleJAX
• #NumericalComputing
• #MachineLearning
• #ScientificComputing
• #PythonProgramming
• #GPUPerformance
• #TPUAcceleration
• #Autograd
• #JAXLibrary
• #DeepLearning