Golang As The New Machine Learning Powerforce

Introducing an ML framework written in Go from scratch.

Introducing an ML framework written in Go from scratch.

Python is known as the king of data science. Its usage has exploded in the last decade and with ML libraries such as TensorFlow, PyTorch, Caffe and more, it has made code extraordinarily easy to write. The underlying C/C++ software provides the ultimate behind-the-scenes numerical computations, so that a data scientist can focus on the real work. Which is great, but doesn’t change the fact that python is slow.

I’m here to tell you why Go is the new preferred ML programming language and that my keras equivalent neural network architecture will lead the way.

Photo by  Kevin Ku on  Unsplash

I started my programming journey with C++and have been on the verge of creating a game engine. Python, on the other hand, was something I’ve always tried to avoid. Moving from a low level language to a more abstract way of doing programming, was a big revelation. Go brings best of both the worlds, like lower level languages like C/C++, Go is a compiled language. That means performance is almost nearer to lower level languages. It also uses garbage collection to allocation and removal of the object. And, like Python its code is easy, fun and handles concurrency like no other.

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Static code analysis is a method of debugging by examining source code before a program is run. It's done by analyzing a set of code against a set (or multiple sets) of coding rules. Static code analysis and static analysis are often used interchangeably, along with source code analysis.