Leszek-Sieminski / Deep-Learning-in-R

Workshop (6 hours): Deep learning in R using Keras

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.

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 (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

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:

• R version 3.4 or greater
• RStudio editor is highly recommended but not required.

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

• 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
  • Charniak E - Introduction to Deep Learning
  • I. Goodfellow, Y. Bengio, A. Courville - www.deeplearningbook.org
  • Zhang et al. - Dive into Deep Learning

• Python

  • Qingkai Kong's Artificial Neural Network basics in Python