How to make HTTP server for prediction of machine learning model with Julia

Abstract

On this article, I'll try Julia's HTTP server. Concretely, the goal is to make HTTP server that execute k-means’s prediction. About machine learning task, it is usual to set the learned model to HTTP server and post the data to that. So, as a first step of it on Julia, I'll try it. The package used here is HTTP.jl.
Here, I'll just touch the initial step and won’t follow the good or proper manner. When you make the HTTP server for machine learning task, I strongly recommend that you read the official document after this article.

Image segmentation and compression by K-means

Abstract

On this article, I'll try image segmentation and compression by K-means.
This is shown on the popular book, Pattern Recognition And Machine Learning, as an example of K-means. I've never used K-means with setting the segmentation and compression of images as a main purpose, because it is not practical way. But it looks fun on the book. So, I'll try.

Here, for experiment, the code is written in Julia.

EM algorithm with Initialization by K-means

Abstract

On this article, I'll check the EM algorithm with the initialized values by k-means. In many algorithms, initial values are very important theme. On EM algorithm, with inappropriate initial values, it takes much time for convergence and if the algorithm is naively written, it stops with error because of non-positive-definite.

So, I'll do experiment to check how much the accuracy, the number of iteration and time change with and without initialized values by k-means. Here, I'll just touch EM algorithm about the mixture of Gaussian case.
If there is a mistake or inappropriate points, please let me know by comment.

k-means++: Introduction and small experiment with Julia

Overview

On this article, I'll write about k-means++. To say precisely, I'll explain what k-means++ is and do small experiment with Julia. For the people who have experience of Python, Julia code is easy to read. So, basically no problem.
k-means++ is regarded as the algorithm to give nice initialization for k-means and sometimes can be used in other algorithm like EM algorithm.
If you find a mistake, please let me know on comment.

Introduction to K-means: Algorithm and Visualization with Julia from scratch

Abstract

On this article, I'll write K-means with Julia from scratch and show animation to see how the algorithm works.
K-means is very simple unsupervised algorithm for clustering. So, when I start to study new programming language, I always use K-means as the theme for writing from scratch.
The following GIF shows how data points are classified into clusters on the way of algorithm going. Relatively easily, we can write K-means code and plot this kind of animation with Julia.



On this article, I used Julia with version 0.6.2.

Speed up naive kNN by the concept of kmeans

Overview

About prediction, kNN(k nearest neighbors) is very slow algorithm, because it calculates all the distances between predict target and training data point on the predict phase.
By adding some process, I tried to make the naive kNN speed up and checked how much the time and accuracy changes.

kmeans by Golang from scratch

Overview

Here, I introduced how to write kmeans, one of the machine learning algorithms, on Go. I’m almost new to Go and this is coding exercise for me through machine learning algorithms.
Go has some machine learning packages. But I couldn’t find the information about how to write machine learning algorithms from scratch. So, I stepwise introduce those.
Those I write here is not for practical use but for understanding algorithms by reading and writing, leading me to set priority on making the code simpler by sacrificing accuracy-improving elements if those are not very easy.