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SciPy 2016: "Data Science is Software" tutorial. From German Tanks to stochastic fortune cookies. Solving the Bayesian German Tank problem with PyMC and PyStan. Speeding up non-vectorizable code with Cython. Code in "Blackhat" is hilariously wrong but recognizable Python. Combinatorial optimization, deterministic and stochastic approaches. Looking at (small) data from five years of driving. My talk on scikit-learn at Statistical Programming DC. Frogs, flies, and Julia. Using Python for linear optimization.

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SciPy 2016: Data Science is Software tutorial. From German Tanks to stochastic fortune cookies. Solving the Bayesian German Tank problem with PyMC and PyStan. Speeding up non-vectorizable code with Cython. Code in Blackhat is hilariously wrong but recognizable Python. Combinatorial optimization, deterministic and stochastic approaches. Looking at (small) data from five years of driving. My talk on scikit-learn at Statistical Programming DC. Frogs, flies, and Julia. Using Python for linear optimization.

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Home | Isaac Slavitt | isaacslavitt.com Reviews

https://isaacslavitt.com

SciPy 2016: "Data Science is Software" tutorial. From German Tanks to stochastic fortune cookies. Solving the Bayesian German Tank problem with PyMC and PyStan. Speeding up non-vectorizable code with Cython. Code in "Blackhat" is hilariously wrong but recognizable Python. Combinatorial optimization, deterministic and stochastic approaches. Looking at (small) data from five years of driving. My talk on scikit-learn at Statistical Programming DC. Frogs, flies, and Julia. Using Python for linear optimization.

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In-mapper combiner pattern for MapReduce | Isaac Slavitt

http://isaacslavitt.com/2014/01/01/in-mapper-combiner-pattern-for-mapreduce

In-mapper combiner pattern for MapReduce. A few days ago, I started reading a really interesting book called Data-Intensive Text Processing with MapReduce (free version is available here. 1] In it, the authors discuss some common patterns for writing MapReduce jobs. One of these patterns is in-mapper combining. In its most basic form, this strategy is designed to save time by doing some aggregation within the mapper instead of emitting tons of. Pairs which will bog everything down in the shuffle-and-sort.

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Combinatorial optimization, deterministic and stochastic approaches | Isaac Slavitt

http://isaacslavitt.com/2015/02/26/combinatorial-optimization

Combinatorial optimization, deterministic and stochastic approaches. Heres another nice PuzzlOR problem. Which lets us look at different ways to tackle an optimization problem, including deterministic and stochastic approaches. A new city is being built which will include 20 distinct neighborhoods as shown by the house icons in the map. As part of the planning process, electricity needs to be connected to each of the neighborhoods. Here are the starting positions of the neighborhoods in array form:.

3

Using Pelican to blog with IJulia notebooks (or other languages) | Isaac Slavitt

http://isaacslavitt.com/2014/09/20/pelican-notebooks-with-ijulia-and-other-languages

Using Pelican to blog with IJulia notebooks (or other languages). To publish this blog, I've been using the Pelican. Static blogging package. And because working in the IPython notebook. Is such a great experience for scientific computing, the. Has been indispensable. (Jake has a really interesting blog. Using the plugin, you can do all of your work in a notebook — writing the content in Markdown. For the last post. I decided to solve a math puzzle in Julia. Thanks to the IJulia. The code was reviewed an...

4

My talk on scikit-learn at Statistical Programming DC | Isaac Slavitt

http://isaacslavitt.com/2014/10/24/spdc-lightning-talk

My talk on scikit-learn at Statistical Programming DC. At Statistical Programming DC. On 10/23/2014 — lightly edited for compatibility with blog format. See the GitHub repo for the original. Modeling with scikit-learn: common workflows for reproducible results. Repo: https:/ github.com/isms/spdc-lightning-talk-oct-2014. View notebook online (non-interactive): http:/ bit.ly/spdc23oct. Describe an example problem. Build a couple basic models. Solidifying the experiments into a repeatable data pipeline.

5

Speeding up non-vectorizable code with Cython | Isaac Slavitt

http://isaacslavitt.com/2015/08/08/speeding-up-with-cython

Speeding up non-vectorizable code with Cython. Most people know that when working with numeric arrays in Python, it is almost always faster to use vectorized operations instead of loops. But what happens when theres no obvious way to vectorize a slow function? There are many approaches for speeding up code, and plenty of other. On the subject. This is just a simple example with Cython. Lets take an arbitrary example. Of some function with logic that makes it annoying to vectorize:. Get random col name.

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Home | Isaac Slavitt

SciPy 2016: "Data Science is Software" tutorial. From German Tanks to stochastic fortune cookies. Solving the Bayesian German Tank problem with PyMC and PyStan. Speeding up non-vectorizable code with Cython. Code in "Blackhat" is hilariously wrong but recognizable Python. Combinatorial optimization, deterministic and stochastic approaches. Looking at (small) data from five years of driving. My talk on scikit-learn at Statistical Programming DC. Frogs, flies, and Julia. Using Python for linear optimization.

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