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Neural Networks in R

In this episode, I dive into my very first hands-on experience building a neural network in R—complete with the raw, messy, exciting learning process. I walk through loading and preparing data, constructing a sequential model with Keras, understanding epochs, loss, and validation error, and even visualizing predictions and computing variable importance. Along the way, I share my thoughts as a statistician exploring machine learning and reflect on what neural networks might offer beyond traditional models like random forests. If you're curious about how to think about neural nets, debug your R+Keras setup, or understand what your model is doing under the hood, this is for you. For my August office hours course: https://simplistics.net/course/office... For my October Machine Learning/Visualization Class: https://simplistics.net/course/data-v... For my September mixed models course: https://simplistics.net/course/introd... For the self-guided R course: https://simplistics.net/course/introd... For the self-guided simplistics course: https://simplistics.net/course/univar... For other classes, see: https://simplistics.net/all-courses/ For consulting, see: https://simplistics.net/product/stati... The dataset comes from this paper: https://journals.plos.org/plosone/art... For my previous video on neural networks, see    • Neural Networks Explained by a Skeptical S...