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ML

Using PyQt with QtAgg in Jupyterlab – IV – simple PyQt and MPL application with background worker and receiver threads

As you read this post you are probably interested in Machine Learning [ML] and hopefully in Linux systems as a ML-platform as well. This post series wants to guide you over a bridge between the standard tool-set of Python3 notebooks in Jupyterlab for the control of ML-algorithms and graphical Qt-applications on your Linux desktop. The objective is to become more independent of some limitations of the browser based Jupyterlab notebooks. One aspect is the use of graphical Qt-based control elements (as e.g. buttons, etc.) in… Read More »Using PyQt with QtAgg in Jupyterlab – IV – simple PyQt and MPL application with background worker and receiver threads

The Meaning of Object Features in different ML-Contexts

When I gave a few introductory courses on basic Machine Learning [ML] algorithms in 2022, I sometimes ran into a discussion about “features“. The discussions were not only triggered by my personal definition, but also by some introductory books on ML the attendants had read. Across such textbooks, but even in a single book on ML the authors have a tendency to use the term “features” in different contexts of ML-algorithms and in particular Artificial Neural Networks [ANN]. Unfortunately, the meaning of the term is… Read More »The Meaning of Object Features in different ML-Contexts

Preliminary test of a Nvidia RTX 4060 TI 16GB with neural networks

Recently I had the opportunity to test a Nvidia RTX 4060 TI (vendor: MSI, model:Ventus ) on my Linux system against a Geforce GTX 960. For private consumers as me who are not interested in gaming, but in Machine Learning [ML] this type of card can be interesting. I name three reasons: Some of the readers of this blog may miss a criterion like “performance“. The reason is that I regard the VRAM criterion as more important as raw GPU power. I have commented on… Read More »Preliminary test of a Nvidia RTX 4060 TI 16GB with neural networks