On this article, I'll write down the Julia's basic dataframe manipulation.
I just started to use Julia. I want to continue to use this language as DS and ML tool. For that, at first I'll focus on the basic dataframe manipulation.
Here, the version of Julia is 0.6.2.

I started to use Julia. Although more or less one year ago, I touched Julia, it didn't become my daily tool. But these days I frequently hear about Julia in data science field and also the news about version 1.0 hit on me. So, I decided earnestly to tackle with it.
On this article, I'll arrange some information of Julia and show some mathematical and matrix operations.

On this article, I'll try four image classification models, vgg16, vgg19, inception-v3 and xception with fine tuning. With Keras, we can easily try this.
About fine tuning itself, please check the article below.

On this article, I'll write about regularization.
Regularization is important method to prevent from overfitting and used in many algorithms. If I try to write accurately in good manner, it will be so long article. So here, I don’t stick to mathematical accuracy so much and in rough way I’ll try to show the system.

As a different approach to overfitting, Dropout is also nice. About this, please check the article below.

Actually, I already wrote the article, Simple regression by Edward: variational inference. There, I re-wrote the model on Edward. But at that time, I used variational bayesian method for inference.
Stan uses Hamiltonian Monte Carlo. So, this time, I'll use Hamiltonian Monte Carlo on Edward and re-write the model.

On this article, I’ll leave the simple memo to get bayesian prediction interval from the sampled points of PyStan output and visualize it.
Basically, I’ll use the code from the article, Simple Bayesian modeling by Stan.

These days, I have watched some videos about time series analysis. I'll write here about one of my recommendations.
Roughly, the video is about time series data manipulation on Python, to say more concretely, on Pandas.

On this video, you can know

how to deal with time stamp data type

simple time series data processing

Those are fundamental and necessary knowledges to deal with time series data on Python. This video lets us know those manipulation on Pandas.
If you are new to time series analysis on Python, you can get the knowledge about basic manipulation by this video.

On time-series analytics, we frequently need to think about a recurring pattern. In the context of time-series analytics, a recurring pattern is referred to as a seasonal effect.

For example, please see the image below. This image is the plotting of a time series data. As you can see, it has recurrent pattern.

On this article, I'll make the local level model with seasonal effects on Stan.

By air passengers data, which is typical time-series data, I'll try some time-series analytics methods. Actually, about some points, I'm not sure if it is really appropriate or not. So, if you find some wrong or incorrect points, please let me know.