Hello! Welcome to the seventh R code walkthrough of the series Machine Learning Foundations where Laurence Moroney, a Developer Advocate at Google working on Artificial Intelligence, takes us through the fundamentals of building machine learned models using TensorFlow.

In this episode, we look at how one can use image augmentation as a technique to artificially extend datasets to provide new information for the training of a neural network. This can potentially help with overfitting issues!

Like the previous R Notebooks, this Notebook tries to replicate the Python Notebook used for this episode.

You can find the R blog post of this code on Rpubs.