Comprehensive JAX implementation of neural networks and scientific computing. Features distributed training, physics-informed networks, custom autodiff, and advanced optimization. Production-ready code with numerical stability, multi-device parallelism, and research-grade implementations.
machine-learning research deep-learning optimization automatic-differentiation parallel-computing transformers bayesian-methods scientific-computing neural-networks high-performance-computing differential-equations gpu-computing pmap distributed-training jax mixed-precision physics-informed pjit tpu-computing
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Updated
Sep 1, 2025 - Jupyter Notebook