Wednesday, May 30, 2018
Sunday, May 27, 2018
Deep learning with Julia: introduction to Flux
Abstract
On this article, I'll try simple regression and classification with Flux, one of the deep learning packages of Julia.Monday, May 21, 2018
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.
Friday, May 11, 2018
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.
Wednesday, May 9, 2018
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.
Saturday, May 5, 2018
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.
Friday, April 27, 2018
Probabilistic Clustering with EM algorithm: Algorithm and Visualization with Julia from scratch
Abstract
On this article, I'll write probabilistic clustering by EM algorithm from scratch with Julia. Here, I'll touch only about mixture of Gaussian case.The outcome of clustering becomes below. To the simple artificial data, it is working.
Wednesday, April 18, 2018
Introduction to K-medoids: Algorithm and Visualization with Julia from scratch
Abstract
On this article, I'll write K-medoids with Julia from scratch.Although K-medoids is not so popular algorithm if you compare with K-means, this is simple and strong clustering method like K-means. So, here, as an introduction, I'll show the theory of K-medoids and write it with Julia.
As a goal, I'll make animation like below.
Sunday, April 8, 2018
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.
Monday, April 2, 2018
Note for speeding up Julia's code
Abstract
On this article, I'll re-write the Julia code, which I wrote before, for speed up.One of the huge advantages of Julia is its performance, speed. But to make advantage of it, we need to write the code in proper rule. So, here, I’ll re-write the code to more efficient one.
Sunday, April 1, 2018
kNN by Julia from scratch
Abstract
On this article, I'll write naive kNN algorithm with Julia. Recently, I started to use Julia and need to practice. kNN is relatively simple algorithm and nice for practice. So, here, I'll write simple kNN with Julia.Saturday, March 31, 2018
Object detection by CAM with Keras
Abstract
On this article, I'll try CAM(Grad-CAM) to high resolution images. Cam has the potential for object-detection. So, I will make CNN model and by CAM, check if it really works.About CAM(Grad-CAM) itself, I'll recommend the theses below.
- Learning Deep Features for Discriminative Localization
- Grad-CAM: Why did you say that? Visual Explanations from Deep Networks via Gradient-based Localization
Tuesday, March 27, 2018
Class Activation Map with Keras
Abstract
On this article, I'll try CAM, Class Activation Map, to mnist dataset on Keras.
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