Hi! My name is Ankur Gupta. I am a machine learning scientist in the Bay Area. I have a PhD in numerical optimization, statistics, & modeling. I am write about machine learning, statistics, programming, and software. In the past, I used to write code in R, Octave/MATLAB, and a little bit in C, C++. Nowadays, I program mostly in Python and ocassionally in Scala.
On this blog, I publish articles that I believe would save someone else a lot time and effort. In the past, I have mostly blogged about productivity improvement techniques, R, Spark, and Ubuntu. Going forward, you should see more of the same as well as articles about machine learning and statistics.
Feel free to reach out to me! My Twitter handle is ankurIO which may be the best way to reach out to me. For questions on specific posts, please feel free to drop a comment in the posts. Or, you can reach out to me via LinkedIn or GitHub.
| Python | rain | 🌧️ A live example to illustrate python packaging, testing, building, & deploying |
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| flicker | 🔥 Wrapper over Pyspark DataFrame to provide a pandas-like API |
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| pyspark-playground | 🤾♂️ Full-fledged Docker image and cluster to run pyspark |
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| Unbracket | Sublime Text 3 Plugin that allows you to remove curly brackets, square brackets, or parentheses |
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| Web | trio | A simple content-focused Jekyll theme |
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| laplacian | Jekyll theme based on Lagrange |
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| Latex | uwcbethesis | UW CBE Compliant LaTeX Thesis Style |
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| quicknotes | 📕 Latex theme for writing notes |
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Most of my published research until now has been focused on chemical engineering, statistics, machine learning, and differential equations. You can find more information on my Google Scholar profile.
Asynchronous Programming in Python
is a soon-to-be-completed self-study course that teaches asynchronous python programming in a
more straightforward way by removing the unnecessary historical cruft.
Selected Topics in CS & Math is a to-be-completed self-study course that discusses selected topics in computer science and mathematics, often useful for interview preparation.