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Machine learning codes are usually laden with far too many complexities (hyperparameters, preprocessing and right things coded in wrong places ...). I am on a mission to untangle ML code by logically separating work flow and creating a re-usable framework that you can easily apply to your machine learning tasks involving any type of data.
The distributed statistical machine translation infrastructure consisting of load balancing, text pre/post-processing and translation services. Written in C++ 11 and utilises multicore CPUs by employing multi-threading, allows for secure SSL/TLS communications.