### Neural Network Batch Training Using Python

Our resident data scientist explains how to train neural networks with two popular variations of the back-propagation technique: batch and online.

### Neural Network L2 Regularization Using Python

Our data science expert continues his exploration of neural network programming, explaining how regularization addresses the problem of model overfitting, caused by network overtraining.

### Neural Network Momentum Using Python

With the help of Python and the NumPy add-on package, I'll explain how to implement back-propagation training using momentum.

### Neural Network Cross Entropy Using Python

James McCaffrey uses cross entropy error via Python to train a neural network model for predicting a species of iris flower.

### Neural Network Back-Propagation Using Python

You don't have to resort to writing C++ to work with popular machine learning libraries such as Microsoft's CNTK and Google's TensorFlow. Instead, we'll use some Python and NumPy to tackle the task of training neural networks.

### Neural Networks Using Python and NumPy

With Python and NumPy getting lots of exposure lately, I'll show how to use those tools to build a simple feed-forward neural network.

### This R/S4 Demo Might Take You Out of Your Comfort Zone

Let's explore factor analysis again, this time using the R ability to tap into OOP, but we won't use the RC model.

### Revealing Secrets with R and Factor Analysis

Let's use this classical statistics technique -- and some R, of course -- to get to some of the latent variables hiding in your data.

### R Language OOP Using S3

The S3 OOP model is still widely used, so let's use write S3-style OOP code via the R language.

### Logistic Regression Using R

I predict you'll find this logistic regression example with R to be helpful for gleaning useful information from common binary classification problems.

### Neural Networks Using the R nnet Package

The R language simplifies the creation of neural network classifiers with an add-on that lays all the groundwork.

### Results Are in -- the Sign Test Using R

The R language can be used to perform a sign test, which is handy for comparing "before and after" data.

### R Language Searching and Sorting

A language that's data-intensive naturally should have a way to dig into the data effectively. Here's a look at some of the R functions for searching and sorting through it all.

### R Language Basic Data Structures

Vectors, lists, arrays, matrices and data frames -- a look at five of the most fundamental data structures built into R.

### Permutations Using R

R has limited support for mathematical permutations, but it's there. Here's what R is capable of accomplishing.

### How the R Language Does OOP

It's not quite like C# or Python, but the R language's object-oriented programming capabilities are getting better with each iteration. Let's take a look at what .NET developers are able do now with OOP in R6.

### Classic Stats, Or What ANOVA with R Is All About

New to this type of analysis? It's a classic statistics technique that is still useful. Here's a technique for doing a one-way ANOVA using R.

### Program-Defined Functions in R

The three most common open source technologies for writing data science programs are Python, SciLab, and R. Here's how to write program-defined functions in R.

### Chi-Square Tests Using R

R is the perfect language for creating a variety of chi-square tests, which are used to perform statistical analyses of counts of data. Here's how, with some sample code.

### Fundamentals of T-Test Using R

Linear regression was easy, right? Now, let's check out t-test analysis using R.

### Linear Regression with R

Now that you've got a good sense of how to "speak" R, let's use it with linear regression to make distinctive predictions.

### Introduction to R for .NET Developers

C# developers who want to wring more meaningful info from large sets of data should get cozy with the statistical computing language known as R. Let's get familiar with R in this new series.