Qt Contributors Summit 2019 Program/Qt Machine Learning and Math

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;