## Recently Published

##### Logistic regression as a single layer network

The last puzzle piece required to understand the fundamentals of deep neural networks is introduced in this post. It considers expressing the predicted variable as a probability so that it can be used in classification problems.

##### Multiple linear regression as a shallow network

This chapter describes a multivariable linear regression model as a neural network with a single hidden layer. This is done so as to create a familiarity with the terms and processes of deep learning.

##### Linear regression as a simple learning network

An example of linear regression serves to illustrate the basic concepts of deep learning through the explanation of terms such as cost functions, backpropagation, global minima, and gradient descent.

##### Regression as a first step in understanding deep learning

This post explains the concepts of a model and an error through the use of linear regression. These concepts will play an important role in creating deep neural networks.

##### Introducing Keras for deep learning

This post introduces Keras in R. A deep learning framework using Google's TensorFlow backend.
The example is a multi-class classification problem from the University of California at Irvine database for machine learning. The dataset is converted to a .csv file and is available in my GitHub repository.

##### Class imbalance

This post describes the ROSE package used to correct for class imbalance.

##### Maternal mortality using World Bank Open Data

This R-markdown post introduces the WDI library to interact with the World Bank Open Data API.

##### Working with bivariate data in R

Describing the expression and visualization of bivariate categorical and numerical variables.

##### Prediction intervals

A short description of prediction intervals, with an example.

##### Scatter plots using Plotly

Scatter plots and bubble charts using Plotly for R.

##### Working with univariate data

Describing and visualizing univariate data in R.

##### Histograms using Plotly for R

A post on the use of Plotly to create histograms in R

##### Bar chart using Plotly

Create bar charts using Plotly for R.

##### Starting with ggplot2

Get going with plotting using ggplot2.

##### Logistic regression using R

This post describes the principals of multivariate logistic regression using R

##### Biserial correlation in R

Biserial and point-biserial correlations allows for the calculation of a correlation coefficient if one of the variables is discrete in nature.

##### Testing assumptions for the use of parametric tests

In this post with R code snippets, I discuss some of the assumptions that must be met for the use of parametric tests