Deep Learning for Everyone (Rday Medellin)

Deep Learning for Everyone (Rday Medellin)

As a Data Scientist (or aspiring Data Scientist) we are overwhelmed by the amount of knowledge we need to have and acquire. Every day there is a new technique, a new framework, a new state of the art model. For the last few years, Deep Learning has become a hot topic and it is the main driver of many applications. But how can we start our Deep Learning journey? Which of the several deep learning frameworks should we use? Where can I find examples of code that work and that I can use without worrying about the license?

In this talk, I will show you how you can start with Deep Learning without any previous Deep Learning knowledge and how you can have a basic ready-to-use deep learning “service” running in less than five minutes.


Gabriela de Queiroz

November 08, 2019


  1. Deep Learning for Everyone Gabriela de Queiroz Sr. Engineering &

    Data Science Manager, IBM Founder, R-Ladies & AI Inclusive @gdequeiroz | slides:
  2. Gabriela de Queiroz • Founder of R-Ladies • Founder of

    AI Inclusive ( • Member of the R Foundation • Sr. Engineering & Data Science Manager, IBM Data Scientist + Developer Advocate + Open Source Developer + Manager + Community Builder + Mentor slides:
  3. slides: Worldwide organization that promotes diversity in the #rstats

    community via meetups and mentorship in a friendly and safe environment.
  4. slides: 2012 - From Brazil to San Francisco

  5. 5 San Francisco, CA October 2012

  6. slides: @gdequeiroz |

  7. Center for Open Source Data and AI Technologies (CODAIT) 30+

    open source developers! Watson West Building 505 Howard St. San Francisco, California Improving Enterprise AI lifecycle in Open Source Gather Data Analyze Data Machine Learning Deep Learning Deploy Model Maintain Model Python Data Science Stack Fabric for Deep Learning (FfDL) Mleap + PFA Scikit-Learn Pandas Apache Spark Apache Spark Jupyter Model Asset eXchange Keras + Tensorflow CODAIT Gather Data Analyze Data Machine Learning Deep Learning Deploy Model Maintain Model Python Data Science Stack Fabric for Deep Learning (FfDL) PFA, PMML, ONNX Scikit-Learn Pandas Apache Spark Jupyter Model Asset eXchange (MAX) Tensorflow + PyTorch AIF360 ART AIF360 ART AIF360 ART Apache Spark Data Asset eXchange (DAX) Build tools to make AI accessible to all @gdequeiroz |
  8. None
  9. ❗

  10. > 4 million results! > 183 million results!

  11. slides: Help!

  12. Model Asset eXchange Place for developers/data scientists to find and

    use free and open source deep learning models @gdequeiroz |
  13. 30+ ready to use deep learning models

  14. Model Asset eXchange (MAX) • Wide variety of domains (text,

    audio, image, etc) • Multiple deep learning frameworks (TensorFlow, 
 PyTorch, Keras) • Trainable and Deployable versions
  15. What do I need to get started?


  17. Ways of accessing the models

  18. OBJECT DETECTOR Localize and identify multiple objects in a single

    image @gdequeiroz |

  20. None
  21. Access the API via Swagger

  22. Access the API via Python Try yourself here:

  23. Access the API via R slides:

  24. Access the API via Web App Try yourself here:

  25. Access the API via Node-RED flow

  26. Access the API via CodePen

  27. All this in a standardized way

  28. None
  29. Find* a state-of-art open source deep learning model specific to

    domain Validate license terms Perform model health check & code clean up Wrap models in MAX framework and provide REST API Publish the deployable model as Docker images on Docker Hub Use the MAX training framework to create an image for custom model training Review and Continuous Integration * or build from scratch BEHIND THE SCENES
  30. And if you are feeling adventurous…

  31. You can train your model using your own data

  32. None
  33. @gdequeiroz |

  34. How do I get started?

  35. @gdequeiroz |

  36. Code Patterns How to easily consume MAX models

  37. Would you like to contribute? Check our central repository containing

    all details about contribution.
  38. Ideas for contribution: 1) New model using the Model Asset

    Exchange Skeleon
  39. Ideas for contribution: 2) Demo notebooks in Python

  40. Ideas for contribution: 3) Demo notebooks (.Rmd for example) in

    R 4) Shiny Apps
  41. Photo by Peter Adams - Check this out: I'm

    the first one to be representing #rstats #rladies Thank you! K-ROZ .COM @GDEQUEIROZ