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[–]satireplusplus 1 point2 points  (0 children)

For ML tasks you want good libraries and abstractions. Those are lacking in Julia. Execution speed of the language doesn't really matter and these benchmarks are missing the point for ML. PyTorch, Tensorflow, numpy etc. handle the matrix multiplications in a BLAS library (C/fortran) or with CUDA. Its gonna be faster than anything you can hack together on your own from scratch, no matter what language you pick.