{"id":13665863,"url":"https://github.com/dlab-berkeley/R-Deep-Learning","last_synced_at":"2025-04-26T08:33:31.273Z","repository":{"id":46057404,"uuid":"159274630","full_name":"dlab-berkeley/R-Deep-Learning","owner":"dlab-berkeley","description":"Workshop (6 hours): Deep learning in R using Keras. 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First, we will cover the basics of what makes deep learning\n\"deep.\" Then, we will explore using code to classify images. Along the way, we\nwill build a workflow of a deep learning project. \n\n## Installation Instructions\n\nWe will use RStudio to go through the workshop materials, which requires\ninstallation of R, RStudio, and TensorFlow. Complete the following steps if you\nwant to work locally. \n\n1. Download [R](https://cloud.r-project.org/) and\n   [RStudio](https://www.rstudio.com/products/rstudio/download/)\n\n2. Within the R console, run the following commands \n\n```\ninstall.packages(c(\"tensorflow\", \"keras\", \"reticulate\")) # Pulls in all R dependencies necessary for TensorFlow in R\n\nlibrary(reticulate)\n\n# Set up R with a Python installation it can use\nvirtualenv_create(\"r-reticulate\", python = install_python()) \n\nlibrary(keras)\ninstall_keras(envname = \"r-reticulate\") # Install TensorFlow and Keras python modules\n```\n\nAfter these steps you will have a working Keras and TensorFlow installation.\nThis process will take some time if you decide to download to your local\nmachine. To determine the TensorFlow version installed on your machine, run in\nthe console\n\n```\nlibrary(tensorflow)\ntf$constant(\"Hello Tensorflow!\")\n```\n\n3. Install additional packages required for this workshop\n\n```\ninstall.packages(c(\"tfhub\", \"tfdatasets\")\n```\n\n# About the UC Berkeley D-Lab\n\nD-Lab works with Berkeley faculty, research staff, and students to advance\ndata-intensive social science and humanities research. Our goal at D-Lab is to\nprovide practical training, staff support, resources, and space to enable you to\nuse R for your own research applications. Our services cater to all skill levels\nand no programming, statistical, or computer science backgrounds are necessary.\nWe offer these services in the form of workshops, one-to-one consulting, and\nworking groups that cover a variety of research topics, digital tools, and\nprogramming languages.  \n\nVisit the [D-Lab homepage](https://dlab.berkeley.edu/) to learn more about us.\nYou can view our [calendar](https://dlab.berkeley.edu/events/calendar) for\nupcoming events, learn about how to utilize our\n[consulting](https://dlab.berkeley.edu/consulting) and [data\nservices](https://dlab.berkeley.edu/data), and check out upcoming\n[workshops](https://dlab.berkeley.edu/events/workshops). Subscribe to our\n[newsletter](https://dlab.berkeley.edu/news/weekly-newsletter) to stay up to\ndate on D-Lab events, services, and opportunities.\n\n\n# Additional Resources\n\n* Massive open online courses\n    * [fast.ai - Practical Deep Learning for Coders](https://course.fast.ai/)\n    * [Kaggle Deep Learning](https://www.kaggle.com/learn/deep-learning)\n    * [Google Machine Learning Crash Course](https://developers.google.com/machine-learning/crash-course/)\n    * [See this](https://developers.google.com/machine-learning/crash-course/fitter/graph) sweet interactive learning rate tool\n    * [Google seedbank examples](https://tools.google.com/seedbank/seeds)\n    * [DeepLearning.ai](https://www.deeplearning.ai/)\n    \n* Workshops\n    * [Nvidia's Modeling Time Series Data with Recurrent Neural Networks in Keras](https://courses.nvidia.com/courses/course-v1:DLI+L-HX-05+V1/about)\n\n* Stanford\n    * CS 20 - [Tensorflow for Deep Learning Research](http://web.stanford.edu/class/cs20si/syllabus.html)\n    * CS 230 - [Deep Learning](http://cs230.stanford.edu/)\n    * CS 231n - [Neural Networks for Visual Recognition](http://cs231n.github.io/)\n    * CS 224n - [Natural Language Processing with Deep Learning](http://web.stanford.edu/class/cs224n/)\n\n* Berkeley\n    * Machine Learning at Berkeley [ML@B](https://ml.berkeley.edu/)\n    * CS [189/289A](https://people.eecs.berkeley.edu/~jrs/189/)\n\n* UToronto CSC 321 - [Intro to Deep Learning](http://www.cs.toronto.edu/~rgrosse/courses/csc321_2018/)\n\n* Videos\n    * J.J. Allaire [talk at RStudioConf 2018](https://www.rstudio.com/resources/videos/machine-learning-with-tensorflow-and-r/)\n\n* Books\n    * F. Chollet and J.J. Allaire - [Deep Learning in R](https://www.manning.com/books/deep-learning-with-r)\n    * Charniak E - [Introduction to Deep Learning](https://mitpress.mit.edu/books/introduction-deep-learning)  \n    * I. Goodfellow, Y. Bengio, A. Courville - [www.deeplearningbook.org](https://www.deeplearningbook.org/)\n    * Zhang et al. - [Dive into Deep Learning](http://en.diveintodeeplearning.org/) \n\n# Other D-Lab R workshops\n\nD-Lab offers a variety of R workshops, catered toward different levels of\nexpertise.\n## Introductory Workshops\n\n* [R Data Wrangling](https://github.com/dlab-berkeley/R-Data-Wrangling)\n* [R Data Visualization](https://github.com/dlab-berkeley/R-Data-Visualization)\n* [R Census Data](https://github.com/dlab-berkeley/Census-Data-in-R)\n\n## Intermediate and Advanced Workshops\n* [R Geospatial Fundamentals](https://github.com/dlab-berkeley/R-Geospatial-Fundamentals)\n* [R Machine Learning](https://github.com/dlab-berkeley/R-Machine-Learning)\n* [R Deep Learning](https://github.com/dlab-berkeley/R-Deep-Learning)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdlab-berkeley%2FR-Deep-Learning","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdlab-berkeley%2FR-Deep-Learning","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdlab-berkeley%2FR-Deep-Learning/lists"}