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.
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.
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.
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.
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.
This post describes the ROSE package used to correct for class imbalance.
This R-markdown post introduces the WDI library to interact with the World Bank Open Data API.
Describing the expression and visualization of bivariate categorical and numerical variables.
A short description of prediction intervals, with an example.
Scatter plots and bubble charts using Plotly for R.
Describing and visualizing univariate data in R.
A post on the use of Plotly to create histograms in R
Create bar charts using Plotly for R.
Get going with plotting using ggplot2.
This post describes the principals of multivariate logistic regression using R
Biserial and point-biserial correlations allows for the calculation of a correlation coefficient if one of the variables is discrete in nature.
In this post with R code snippets, I discuss some of the assumptions that must be met for the use of parametric tests