Machine Learning with Go Lang

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Recently I’ve been having a number of conversations with people in several countries about using Go Lang for machine learning. Most of these people have been struggling with using Python for machine learning and are looking for an alternative that will give them better performance. We have been experimenting with C++ and Go Lang to see what the performance differences are. Most of these are with the execution of the ML code. This is great and everyone is very happy with execution timings, compared to Python.

But, there is a flip side to this. Although we have faster execution timings, there is a down side in that the coding effort is higher, with more lines of code and fewer libraries/packages to support the various ML tasks. But most of these can be easily coded ourselves .

We also looked at some frameworks for converting ML models developed in one language but deployed in production using a different language. More on that in another post.

Overall the extra development work was considered worthwhile for the performance improvement and deployment gains.

Go Lang doesn’t really come with it’s own set of libraries/packages for ML, but those have a number of these that can be used to code up the necessary functions we need for our everyday ML needs.

But are there any Go Lang libraries/packages developed for ML, just like we have for the R Language, etc?  The simple answer is YES we have. But the number of these is small in comparison to R and Python. Both of these languages are interpreted languages. But those available for Go are slowly growing.

Here is list of the Go Lang libraries/packages that we examined and evaluated for these projects. Some are available from the Go Lang website/wiki and others are available on Github.

  • Anna – Artificial Neural Network Aspiration, aims to be self-learning and self-improving software.
  • bayesian – A naive bayes classifier.
  • Dialex – Dialex is a smart pipe that unscrambles text and makes it machine-readable.
  • Cloudforest – Ensembles of decision trees
  • ctw – Context Tree Weighting and Rissanen-Langdon Arithmetic Coding
  • eaopt – An evolutionary optimization library.
  • evo – a framework for implementing evolutionary algorithms in Go.
  • gobrain – Neural Networks
  • Go Learn – Machine Learning for Go
  • go-algs/maxflow Maxflow (graph-cuts) energy minimization library.
  • go-graph – Graph library for Go/Golang language
  • go-galib – Genetic algorithms.
  • go-pr – Pattern recognition package in Go lang
  • golinear – Linear SVM and logistic regression.
  • go-mind – A neural network library built in Go
  • go_ml – Linear Regression, Logistic Regression, Neural Networks, Collaborative Filtering, Gaussian Multivariate Distribution.
  • go-ml-transpiler – An open source Go transpiler for machine learning models.
  • go-mxnet-predictor – Go binding for MXNet c_predict_api to do inference with pre-trained model.
  • gorgonia – Neural network primitives library (like Theano or Tensorflow but for Go)
  • go-porterstemmer – An efficient native Go clean room implementation of the Porter Stemming algorithm.
  • go-pr – Gaussian classifier.
  • ntmNeural Turing Machines implementation
  • paicehusk – Go implementation of the Paice/Husk Stemmer
  • RF – Random forests implementation in Go
  • tfgo – Tensorflow + Go, the gopher way.

 

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