A famous satire provides unexpectedly useful baseline criteria for new intellectual frameworks.
Some quick tips for preparing slide presentations that include source code.
There may be better ways to end your talk than (merely) thanking your audience.
Some notes from setting up a local Kubernetes environment for accelerated data science.
Parquet is available in many environments but you’ll need to keep some quirks in mind to realize the benefits of its ubiquity.
A virtual handout for our KubeCon talk.
A good map can reveal a lot about a problem and its solution.
My argument for a new way to think about machine learning systems in the cloud.
Altair is one of my favorite plotting libraries. Here are some examples of how to use it for data prep, interactive plots, and geospatial data.
Avoid a performance pitfall when using SciPy’s probability distributions.
Materials from a tutorial on some very cool data structures.
What do your results tell you about the world?
A world in which anyone can build a Linux container image is also a world in which everyone is maintaining their own Linux distribution, whether they want to or not.
Some materials related to my quick overview of machine learning techniques for enterprise developers at Red Hat Summit 2018.
Some materials and links related to my talk on probabilistic data stuctures.
It’s possible to approximate the module dependencies of Python code with lightweight static analysis. These approximations aren’t perfect, but they are useful.
If you resist the temptation to start too quickly, you can cover more ground.
InputFormat
OutputFormat
sbt
leitmotif
sbt console
version
Node#ge…
gliss
wallaby console
Visitar URL original