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