JAX
Frameworks
JAX

Provide documentation and tutorials for the JAX library, used for high-performance numerical computation and machine learning research.

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【Application Scenarios】

  • High-performance numerical computation
  • Large-scale machine learning

【Target Users】

  • Researchers
  • Engineers

【Core Features】

  • Provide NumPy-style API
  • Support for compilation, batch processing, automatic differentiation, and parallelizable function transformations
  • Support for execution on CPU, GPU, and TPU

【Is it free】

  • Yes

【Community Ecosystem】

  • Machine learning tool ecosystem developed around JAX
  • Includes neural network frameworks, optimizers and solvers, data loading tools, etc.

【Summary】

  • JAX is a Python library focused on efficient array operations and program transformations, suitable for high-performance numerical computation and large-scale machine learning.
  • It provides a familiar NumPy-style API, supports multiple backends for execution, and has a rich community ecosystem.
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