Qt Contributors Summit 2019 Program/Qt Machine Learning and Math

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Introduction

  • Slides:
  • The Python modules that are used in ML have C++ implementations under the hood. Here are some examples:
    • TensorFLow
    • Keras
    • Scikit-learn
    • Theano
    • PyTorch
  • In DS the main Python modules are:
    • NumPy
    • Pandas
  • Data science work consists in Data Extracting, transforming it, loading it and cleaning it, and just then they apply ML techniques;
  • Math in C++:
    • cmath
    • Armadillo
    • We don't have math modules in Qt because it's hard to have something so specific;

Thoughts on how Qt and Qt for Python fit in this market

  • Make the interface interaction with Qt and these libraries
    • The point is not reimplementing data frames or numpy stuff, we want to add support to it on PySide, having widgets ready to use;
  • PyTorch offers some support that'd make it easy to integrate with Qt;
  • Create training interfaces [...];
  • Show insides of a module like TensorFlow it's doing in its website;
  • Important to ask ourselves what are we bringing to the table, because there is a lot of things that are already out there doing a pretty good job;
    • It's hard to get in to a market that's already established;
  • Offer a tool that allows data scientists to interact with the data and modify it;
  • If we had a better graphical display system in Qt it'd be easier to implement this kind of thing. Maybe the answer is to fix QtChart, or maybe the answer is to kill QtChart;
    • We could provide building blocks to people to build their own dashboard;
      • It'd help a lot the DS people to visualize their data, helping them cleaning it;