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Deep Learning in R

This is the repository for D-Lab’s six-hour Introduction to Deep Learning in R workshop. View the associated slides here.



Convey the basics of deep learning in R using keras on image datasets. Students are empowered with a general grasp of deep learning, example code that they can modify, a working computational environment, and resources for further study.

Content outline

  • Installation
    • R and RStudio
    • Keras and Tensorflow
    • Helper packages
  • What is “deep” learning?
  • Understanding the dataset
  • Dataset splitting: training, test, cross-validation
    • Defining moving parts of a deep learning model
    • Understanding a loss function, activation function, and metrics
    • Performance evaluation
  • Part 1-2
    • MNIST 0-9 hand-written digit example
    • Dogs or humans?
  • Part 3-4
    • Pre-trained models + fine-tuning
    • X-ray classification: abdominal vs. chest classification
    • Google Cloud Machine Learning


This is an advanced level workshop. Participants should be intermediate R users and have had some prior exposure to machine learning.

We assume the following background:

  • D-Lab's Machine Learning in R introduction (6 hours) or its tidymodels adaptation
  • Or, comparable experience/training, assuming familiarity with:
    • Basic R syntax
    • statistical concepts such as mean and standard deviation
    • Train/test splitting and cross-validation
    • Dataset cleaning
    • Overfitting / underfitting
    • Hyperparameter customization

If you are not comfortable installing packages, writing your own R code, and using RStudio, this will not be a good workshop for you.

Technology requirements

Please bring a laptop with the following:

Getting Started

Be sure to follow the install instructions to get started. This process can take about 30 minutes, so be sure to try and do this before class.



Workshop (6 hours): Deep learning in R using Keras. Building & training deep nets, image classification, transfer learning, text analysis, visualization




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