Sunday, June 10, 2018

How to write Inception module: understanding and coding with Keras

Abstract

This article covers the basic understanding and coding of Inception module.
GoogLeNet, which is composed by stacking Inception modules, achieved the state-of-the-art in ILSVRC 2014. And probably, many people already touched the models which have the name “Inception” by fine-tuning. Here, on this article, I'll deal with the Inception module.
To write the model, I'll use Keras with Python.
To deepen your knowledge, you can use the following paper.

Tuesday, June 5, 2018

Various ways of writing Neural Network with Flux: to write complex model

Abstract

Flux is one of the deep learning packages in Julia. It is flexible and easy to use. But, there are not enough examples to grasp the points, although the official documents and model zoo somehow work. So, here, on this article, I'll write down some types of model and the points where I was caught. I'm still on the phase of exploring Flux by reading the source code and trial-error. So, if you find something strange or mistake, please let me know.
On this article, I'll use Julia version 0.6.2.


Wednesday, May 30, 2018

Convolutional Neural Network with Julia: Flux

Abstract

Here, I'll make a convolutional neural network model by Flux with Julia. In the article, Deep learning with Julia: introduction to Flux, I made simple neural network with Flux. Neural network, especially convolutional neural network, is quite efficient in image classification area. So, this time, I'll make the convolutional neural network model to image classification.

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.