PyPHM - Machinery data, made easy
Why build an open-source package for machinery data?
• 3 min read
Condition Monitoring Data Science
Machine Learning
Meets Metal
Applied AI. Real-world data. From intelligent agents to industrial systems — practical engineering with data.
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Anomaly Detection - Advances in Condition Monitoring, Pt IV
Machines fail. Anomaly detection, using an autoencoder, can be used to identify failures that have never been seen before.
· 6 min read
Beautiful Plots: The Decision Boundary
How can we communicate complex concepts using data visualization tools? In this first post -- in a series titled 'Beautiful Plots' -- we build an elegant chart demonstrating the decision boundary from a KNN classifier.
· 12 min read
Finding Inspiration in Random Ways
A wellspring of ideas
• 5 min read
Idea Generation Inspiration Creativity
Beautiful Plots: The Violin
Music to my ears... err... eyes? The violin plot is a worthy tool for any data visualization tool box. Let's build one, in Plotly, as we explore historic birth trends in the USA!
• 5 min read
Plotly Violin Plot Data Visualization
Using Jupyter Notebooks on a High Performance Computer - Tutorial
Using Jupyter Notebooks in a high performance computing environment is easy! This tutorial will show you how.
• 1 min read
HPC Jupyter Notebook Tutorial
Analyzing the Results - Advances in Condition Monitoring, Pt VII
We've trained the variational autoencoders, and in this post, we see how the models perform in anomaly detection. We check both the input and latent space anomaly detection effectiveness.
• 27 min read • Part 7 of 7
Precision Recall Curve ROC Curve Machine Learning
Beautiful Plots: The Lollipop
The lollipop chart is great at visualizing differences in variables along a single axis. In this post, we create an elegant lollipop chart, in Matplotlib, to show the differences in model performance.
• 9 min read
Matplotlib Lollipop Chart Data Visualization
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