Update train.c #1
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Extra Parallelism:
I added more OpenMP pragmas across independent loops (bias additions, activation functions, reductions, etc.) to better exploit multi-core processors. This should help speed up training without changing any functionality.
Optimized Matrix Operations:
We’re using our BLAS routines (cblas_sgemm, cblas_saxpy, etc.) more effectively to crunch those matrices, both in the forward and backward passes. This offloads heavy number crunching to optimized libraries, giving us a nice performance boost.
overall its more efficient, faster, and more optimized