Anthony Corletti cloud computing. startups. music. etc.
Posts with the tag go:

## Linear Regression with Go

Linear regression is a common statistical data analysis technique.

It is used to determine the extent to which there is a linear relationship between a dependent variable and one or more independent variables, and is widely used in machine learning applications.

There are two types of linear regression, simple linear regression and multiple linear regression.

In simple linear regression a single independent variable is used to predict the value of a dependent variable. In multiple linear regression two or more independent variables are used to predict the value of a dependent variable. The difference between the two is the number of independent variables. In both cases there is only a single dependent variable.

In this post I’m going to introduce basic concepts on linear regression and build a simple linear regression example using go and spago.

## Question Answering NLP in Go

I’ve wanted to do more software development in go but have found myself bouncing back to python or ruby due to familiarity with libraries, web frameworks, and ML/ AI tools.

About a week ago I stumbled onto, spago a ML library that is written in go that’s designed to support neural network architectures in NLP based tasks.

Figured this is a great way to start teaching myself more about the language given that there are more and more tools like this that are enabling robust ML/ AI applications in golang applications. I’m unsure if anything will be as robust as something like tensorflow or pytorch, but for now working with something like spago and golearn is a great start. See my previous post on building a K-nearest-neighbors implementation with go and golearn.

So let’s walk through an example that illustrates how we can build a simple service that does question answering NLP with spago.

It’s really easy to build a K-nearest-neighbors implementation in go using golearn.
I’m writing this post using go1.14.4 so let’s dive in to installing golearn and writing up a quick example K-nearest-neighbors implementation.