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BlogI tend to blog about technical topics, including interesting mathematics and cool applications of Python. A complete lists of my posts can be found at Blog.
Apple CoreML model conversion
Apple has created a new file format for machine learning models. These files can be used easily to predict, regardless of the creation process, which means that “Apple Introduces Core ML” draws an analogy between these files and PDFs. It’s possible to generate predictions with only this file, and none of the creation libraries.
Generating predictions is a pain point faced by data scientists today and often involves the underlying math. At best, this involves using training the model in Python and then calling the underlying C library in the production app.
This file format will only become widely used if easy conversion from popular machine learning libraries is possible and predictions are simple to generate. Apple made these claims during their WWDC 2017 keynote. I want to investigate their claim.
Read on →
Atmosphere and entropy
I recently learned an abstract mathematical theorem, and stumbled across a remarkably direct measure. I’ll give background to this theorem before introducing it, then I’ll show the direct measure of this theorem with physical data.
This theorem has to do with entropy, which is clouded in mystery. There are several types of entropy and, during the naming of one type, Von Neumann suggested the name “entropy” to Claude Shannon in 1948 because
In the first place your uncertainty function has been used in statistical mechanics under that name, so it already has a name. In the second place, and more important, no one really knows what entropy really is, so in a debate you will always have the advantage.
Motivation for sexual reproduction
Of course, the purpose of sexual reproduction is to perpetuate our species by having offspring. Combined with natural selection, it’s enable fit our genes to our environment quickly. Buy why is it required to have two mates to produce a single offspring? Would asexual reproduction or having 3+ parents be more advantageous?Read on →
Easy powerful parallel code execution and use on a UW cluster
I often have highly optimized code that I want to run independently for different parameters. For example, I might want to see how reconstruction quality varies as I change two parameters. My code takes a moderate amount of time to run, maybe 1 minute. This isn’t huge, but if I want to average performance over 5 random runs for $20^2$ different input combinations, using a naïve for-loop means about 1.5 days. Using dask.distributed, I distribute these independent jobs across different machines and different cores for a significant speedup.Read on →
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