google/jax: GPU- and TPU-backed NumPy with differentiation and JIT compilation.
JAX is a Python library for accelerator-oriented array computation and program transformation, designed for high-performance numerical computing and large-scale machine learning. With its updated version of Autograd, JAX can automatically differentiate native Python and NumPy functions. It can differentiate through loops, branches, recursion, and closures, and it can take derivatives of derivative
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Why the GPGPU is Less Efficient than the TPU for DNNs
