Stats package on Golang

When we deal with data on the meaning of data analysis, data science, and machine learning, Go’s statistics package does a good job.

On the area of data science and machine learning, people usually use Python and R. On the internet, we can see just a few amount of information about data science with Go.

So I’ll leave concise memo about that.
Here I’ll introduce how to use basic statistics package.

How to deal with heteroscedasticity

On the article below, I wrote about heteroscedasticity.

What is heteroscedasticity and How to check it on R

Linear regression with OLS is simple and strong method to analyze data. By the coefficients, we can know the influence each variables have. Although it looks easy to use linear regression with OLS because of the simple system from the viewpoint of necessary code and mathematics, it has some important conditions which should be kept to get proper coefficients and characteristics.

Roughly, with heteroscedasticity, we can’t get OLS’s nice feature, unbiasedness. And plot and some tests such as Breusch-Pagan test reveal the existence of heteroscedasticity.

After knowing the problem, of course we need to know how to solve it.
Here on this article, I’ll write about how to deal with this heteroscedasticity.

I’ll use same data here as the article above.

What is heteroscedasticity and How to check it on R

Linear regression with OLS is simple and strong method to analyze data. By the coefficients, we can know the influence each variables have.

Although it looks easy to use linear regression with OLS because of the simple system from the viewpoint of necessary code and mathematics, it has some important conditions which should be kept to get proper coefficients and characteristics.

On this article, I’ll show the way to check heteroscedasticity.

Simple tutorial to write deep neural network by TensorFlow

Overview

On this article, I’ll show simple deep neural network(DNN) model for regression by TensorFlow.

TensorFlow is open source library from Google. From the official web site,

TensorFlow™ is an open source software library for numerical computation using data flow graphs.

From wikipedia,

TensorFlow is an open-source software library for machine learning across a range of tasks. It is a system for building and training neural networks to detect and decipher patterns and correlations, analogous to (but not the same as) human learning and reasoning.[3] It is used for both research and production at Google,‍[3]:min 0:15/2:17 [4]:p.2 [3]:0:26/2:17 often replacing its closed-source predecessor, DistBelief.

It lets us make neural network relatively easily. But different from keras, this needs proper knowledge of the things you want to make.
This article is almost simple tutorial to make deep neural network model for regression.

You can know the followings on this article.

• What is deep neural network?
• How do we write deep neural network model by TensorFlow

Simple regression model by TensorFlow

Overview

Neural network is composed of input, hidden and output layers. And the number of hidden layers is optional. So the simplest network architecture has just one hidden layer.

On this article, I’ll make the simplest neural network for regression by TensorFlow.

From the official web site,

TensorFlow™ is an open source software library for numerical computation using data flow graphs.

This makes it easier to make shallow and deep neural network and other machine leaning algorithms. Through the simple trial, we can learn about TensorFlow and the system of neural network.

About the Tensorflow itself, please check the article below.

• Simple guide for Tensorflow

VGG16 Fine-tuning model

Overview

On the article, VGG19 Fine-tuning model, I checked VGG19’s architecture and made fine-tuning model. On the same way, I’ll show the architecture VGG16 and make model here.

There are some image classification models we can use for fine-tuning.
Those model's weights are already trained and by small steps, you can make models for your own data.

About the fine-tuning itself, please check the followings.

• How to make Fine tuning model by Keras
• VGG19 Fine-tuning model

Making linear regression model by R

Overview

When we make model by data science, machine learning method, it’s not simple process such as “just throw data into SVM”. Getting, checking, processing, modeling, evaluation. There are many steps you need to care about.

On this article, by making regression model on R, I’ll show the example of part of the process.

Simple guide to kNN

Personally, I like kNN algorithm much. Because kNN, k nearest neighbors, uses simple distance method to classify data, you can use that in the combination with other algorithms. It can also be one of the first step to study machine learning algorithms because of the simplicity.

On the following articles, I wrote about kNN. But although I myself don’t know the reason, I’ve never touched the simplest usage of kNN, meaning how to use kNN of sklearn’s library.

• kNN by Golang from scratch
• How to write kNN by TensorFlow
• Speed up naive kNN by the concept of kmeans
• CNN + KNN model accuracy

How to write kNN by TensorFlow

Overview

How do we write machine learning algorithms with TensorFlow?
I usually use TensorFlow only when I write neural networks. But actually TensorFlow is not only for that. It also can be used to write other machine leaning algorithms.
On this article, I tried to roughly write kNN algorithm by TensorFlow.

The reason to try kaggle and how you do

Overview

I sometimes hear as the answer to the question, “What should I do as study of data science?”, the importance of kaggle.

Personally, I agree with the idea that he/she tries kaggle as early as possible.
Why is kaggle awesome to improve the knowledge of data science/mach learning? I summarize the points of kaggle’s advantages from the viewpoint of studying.

Observation of Beta distribution

Overview

PRML(Pattern Recognition and Machine leaning) is one of the best text books about machine learning. I review it from time to time.
As one of the reviews, I write down some notes of part of it.
This article is about Beta distribution, which is shown on chapter 2 on PRML.

Fashion-MNIST exploring

Overview

Fashion-MNIST is mnist-like image data set. Each data is 28x28 grayscale image associated with fashion. Literally, this is fashion version of mnist.

I'm thinking to use this data set on small experiment from now on. So, for the future, I checked what kind of data fashion-MNIST is.

On the article below, I explored from the viewpoint of Bayes.

Fashion-MNIST exploring using Keras and Edward

On the article, Fashion-MNIST exploring, I concisely explored Fashion-MNIST dataset. We can get access to the dataset from Keras and on this article, I'll try simple classification by Edward. I'll use Fashion-MNIST dataset. Fasion-MNIST is mnist like data set. You can think this as the fashion version of mnist.

VGG19 Fine-tuning model

Overview

On the article, How to make Fine tuning model, I made fine-tuning models by some pre-trained models. At that time, I didn't write about the pre-trained model's architecture and the train target area based on it.

This time, I foucused on the VGG19 as pre-trained model. And in a nutshell, I tried to make fine-tuning model in better manner, checking some important points.