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deep-learning-in-r's Introduction

Deep Learning in R

This is the repository for D-Lab’s Introduction to Deep Learning in R workshop.

Binder

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

  • D-Lab's Machine Learning in R introduction (4 hours)
  • 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

Technology requirements

Please bring a laptop with the following:

Resources

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