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How I built a Claude Code skill for publication-quality matplotlib charts over 50 iterations — and what happened when I automated its improvement.
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Why build an open-source package for machinery data?
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A wellspring of ideas
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Music to my ears... err... eyes?
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Using Jupyter Notebooks in a high performance computing environment is easy!
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We've trained the variational autoencoders, and in this post, we see how the models perform in anomaly detection.
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The lollipop chart is great at visualizing differences in variables along a single axis.
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How can we communicate complex concepts using data visualization tools?
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In this post, we'll explore the variational autoencoder (VAE) and see how we can build one for use on the UC Berkeley milling data set.
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Data exploration is an important first step in any new data science problem.
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Machines fail.
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Feature engineering can be powerful tool, but it doesn't have to be complicated.
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In part two, we give an overview of the two data-driven approaches in condition monitoring; that is, feature engineering and end-to-end deep learning.
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Why care about data-driven condition monitoring techniques?
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What is school for?