Here we see some overfitting case with a lot of visual examples, and explain what you should care about and how to avoid. First we see with traditional statistical regression, and in the latter part we discuss about neural nets.
Here I outline the basic regression ideas of GLM (generalized linear models) for your intuitions with simple R scripts. (For beginners)
In this post I describe the background and how-to for time-series analysis with more practical and advanced topics, non-stationary time-series (ARIMA) and seasonal time-series (Seasonal ARIMA), which is based on the basic idea (knowledge) in my previous post.
Through these posts (part1 and part2), you can shortly understand the outline for ARIMA time-series analysis.
The time-series analysis in statistical learning is frequently needed in the practical system. Here I outline the time-series analysis with ARIMA model for developers building your intuitions.
When you run your R with data in Azure Data Lake, you don’t need to move or download your data. Here I show you how to use R extensions in Azure Data Lake along with the real scenarios.
This post shows the R code of classifying images or matching (searching) similar images with MicrosoftML package. You can easily classify your real photo albums without labeling.
In this post, I show you a brief introduction for the anomaly detection with MicrosoftML.
This shows how you can analyze your text (text featurization) using MicrosoftML package in R with the simple sentiment analytics example.
Here we focus on the MXNet training acceleration: GPU (device) utilized training, distribution training by multiple machines, and active learning (online learning).
Here I show you the step-by-step for scaling the deep learning workloads with MXNet and R. This time, we focus on the scoring phase.