# R and TensorFlow Presentations

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In early March, the Bay Area useR Group was able to hold an R and TensorFlow mini-conference on Google’s new Sunnyvale campus. Pete Mohanty, a Stanford researcher and frequent BARUG speaker, lead off with a talk on his recent kerasformula package, which allows R users to call a keras-based neural net with R formula objects. Pete’s slides show an example of using using a regression-style formula with the `kerasformula::kms()`

function to fit a sequential TensorFlow model.

J.J. Allaire, RStudio’s founder and CEO, spoke for over an hour, delivering a polished and comprehensive presentation that ranged from big-picture vistas illuminating the merits and limitations of the Deep Learning methodology to the deep details of R based TensorFlow models. We were not able to record J.J.’s BARUG talk, but it was similar to his presentation at the January RStudio conference in San Diego, which is well worth watching.

We do have the slides for J.J.’s BARUG presentation. They comprise a comprehensive overview of Deep Learning and the TensorFlow Technology behind it. While most slide presentations are little more than collections of mnemonics, lifeless without the animation of the speaker, J.J.’s 113-slide deck stands on its own. Replete with links to current research papers and references that guide a reader to the frontiers of Deep Learning applications, is a very credible introduction and study guide. Because 113 slide are quite a bit to get through in one sitting, especially if you start following interesting links, I offer the following gloss:

Slide | Topic |
---|---|

3 | What is TensorFlow? |

4 | Why should R users care? |

6 – 10 | What are tensors? |

11 – 13 | What is the “flow”? |

14 – 16 | Applications with links to examples |

17 – 26 | What is Deep Learning, how does it work, and what is deep about it? |

27 | Statistical modeling vs. machine learning with links to seminal papers |

28 – 36 | A technical overview of what goes on in a Deep Learning model |

37 – 44 | Applications on the frontiers of Deep Learning with links to recent papers |

45 – 47 | A perspective on problems, hype and the usefulness of Deep Learning |

48 – 73 | The details of the R interface to Keras and TensorFlow, with links to the |

technical documentation and R packages and code | |

73 – 84 | A tour of the models in RStudio’s TensorFlow Gallery |

85 – 92 | Tools for running on GPUs, managing experiments, running in the cloud, and deploying TensorFlow models |

93 – 101 | cloudml: an interface to Google CloudML |

102 – 112 | R-based technology for deploying TensorFlow models |

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