Machine Learning

It is often advantageous to transform a strongly nonlinear system into a linear one in order to simplify its analysis for prediction and control. Here the authors combine dynamical systems with deep learning to identify these hard-to-find transformations.

This is an illustrated guide for creating data visualizations in R.

R Language Tutorials for Advanced Statistics

Order categorical data in a stacked bar plot with ggplot2 - ***also for side-by-side barplot

Data Visualization

This article contains a flow chart to guide users through the process of choosing a python visualization tool for their visualization needs.

Statistics are one of the most important tools in a data scientist's arsenal. When wading through a positive sea of big data, statistics is the one thing that allows scientists to make sense of it.

Here is the complete list of useful libraries in R to perform data analysis. ggplot2, swirl, shiny, rcmdr, randomforest, survival is listed

Everyone makes mistakes, which can be a good thing when they lead to learning and improvements over time. But, we can also try to first learn from others to expedite our personal growth. To get started, consider these lessons learned the hard way, so you don’t have to.

BarCharts’ handy 6 page guide can make even the most complicated probability equations a breeze. Definitions, hints and sample problems are contained within eac

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This collection covers much more than the topics listed in the title. It also features Azure, Python, Tensorflow, data visualization, and many other cheat sheets. Additional cheat sheets can be found here and here. Below is a screenshot (extract from the data visualization cheat sheet.) The one below is rather interesting too, but the source… Read More »Large Collection of Neural Nets, Numpy, Pandas, Matplotlib, Scikit and ML Cheat Sheets

Researchers at MIT's CSAIL have developed a new technique that identifies high-performance subnetworks within neural networks.