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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.

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Objectives

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

Prerequisites

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.

Resources

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

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