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

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.

• 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

• 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

Please bring a laptop with the following:

• R version 3.4 or greater
• RStudio integrated development environment (IDE) is highly recommended but not required.

• D-Lab Machine Learning Working Group

• Websites

  • RStudio Keras
  • Supplementary notebook materials

• Massive open online courses

  • fast.ai - Practical Deep Learning for Coders
  • Kaggle Deep Learning
  • Google Machine Learning Crash Course
  • See this sweet interactive learning rate tool
  • Google seedbank examples
  • DeepLearning.ai

• Stanford

  • CS 20 - Tensorflow for Deep Learning Research
  • CS 230 - Deep Learning
  • CS 231n - Neural Networks for Visual Recognition
  • CS 224n - Natural Language Processing with Deep Learning

• Berkeley

  • Machine Learning at Berkeley ML@B
  • CS 189/289A

• UToronto CSC 321 - Intro to Deep Learning

• Videos

  • J.J. Allaire talk at RStudioConf 2018

• Books

  • F. Chollet and J.J. Allaire - Deep Learning in R
  • I. Goodfellow, Y. Bengio, A. Courville - www.deeplearningbook.org
  • Zhang et al. - Dive into Deep Learning