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maplib allows you to leverage your existing skills with Pandas or Polars to extract and wrangle data from existing databases and spreadsheets, before applying simple templates to them to build a knowledge graph. You can also read knowledge graphs extremely quickly from a wide variety of serialization formats. Using the built-in SPARQL, SHACL and Datalog engines means you can query, inspect, enrich and validate and then serialize the knowledge graph immediately. All query results are Polars Dataframes that are transferred zero-copy from Rust to Python. Currently, maplib is in-memory and supports around 100M triples on 32GB of RAM.]]></description>
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    <dc:creator>rybesh</dc:creator><description><![CDATA[DrawBot is a powerful, free application for macOS that invites you to write Python scripts to generate two-dimensional graphics. The built-in graphics primitives support rectangles, ovals, (bezier) paths, polygons, text objects, colors, transparency and much more. You can program multi-page documents and stop-motion animations. Export formats include PDF, SVG, PNG, JPEG, TIFF, animated GIF and MP4 video.]]></description>
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    <dc:creator>rybesh</dc:creator><description><![CDATA[It’s not trying to be the next Django or the next Flask or whatever; instead, it feels to me like a Pythonic take on the good parts of something like Spring Boot (and the way I like to set it up, doing things like using svcs behind the scenes as a service locator to feed things to both Litestar’s and pytest’s dependency injection, makes it feel even more that way).]]></description>
<dc:subject>python web</dc:subject>
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    <dc:creator>rybesh</dc:creator><description><![CDATA[In Python, some objects like strs or lists can sliced.]]></description>
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The [build-system] table is strongly recommended. It allows you to declare which build backend you use and which other dependencies are needed to build your project.
The [project] table is the format that most build backends use to specify your project’s basic metadata, such as the dependencies, your name, etc.
The [tool] table has tool-specific subtables, e.g., [tool.hatch], [tool.black], [tool.mypy]. We only touch upon this table here because its contents are defined by each tool. Consult the particular tool’s documentation to know what it can contain.]]></description>
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    <dc:date>2024-04-12T22:09:21+00:00</dc:date>
    <link>https://marimo.io/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[marimo is an open-source reactive notebook for Python — reproducible, git-friendly, executable as a script, and shareable as an app.]]></description>
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    <dc:creator>rybesh</dc:creator><description><![CDATA[Let's take a look at every dunder method in Python, with a focus on when each method is useful.

]]></description>
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    <dc:creator>rybesh</dc:creator><description><![CDATA[You're trying to figure out why your Python code isn't doing what you think it should be doing. You'd love to use a full-fledged debugger with breakpoints and watches, but you can't be bothered to set one up right now.

You want to know which lines are running and which aren't, and what the values of the local variables are.

Most people would use print lines, in strategic locations, some of them showing the values of variables.

snoop lets you do the same, except instead of carefully crafting the right print lines, you just add one decorator line to the function you're interested in. You'll get a play-by-play log of your function, including which lines ran and when, and exactly when local variables were changed.]]></description>
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    <link>https://ml-explore.github.io/mlx/build/html/index.html</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[MLX is a NumPy-like array framework designed for efficient and flexible machine learning on Apple silicon, brought to you by Apple machine learning research.]]></description>
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    <title>Python compiler - visualize, debug, get AI help from ChatGPT</title>
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    <dc:creator>rybesh</dc:creator><description><![CDATA[Online Python compiler, visual debugger, and AI tutor - the only tool that lets you visually debug your code step-by-step]]></description>
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    <title>Welcome to Lark’s documentation! — Lark documentation</title>
    <dc:date>2023-08-14T12:56:46+00:00</dc:date>
    <link>https://lark-parser.readthedocs.io/en/stable/index.html</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Lark is a modern parsing library for Python. Lark can parse any context-free grammar.

Lark provides:

Advanced grammar language, based on EBNF
Three parsing algorithms to choose from: Earley, LALR(1) and CYK
Automatic tree construction, inferred from your grammar
Fast unicode lexer with regexp support, and automatic line-counting]]></description>
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<item rdf:about="https://github.com/karpathy/nanoGPT">
    <title>karpathy/nanoGPT: The simplest, fastest repository for training/finetuning medium-sized GPTs.</title>
    <dc:date>2023-01-04T22:00:49+00:00</dc:date>
    <link>https://github.com/karpathy/nanoGPT</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[The simplest, fastest repository for training/finetuning medium-sized GPTs. It's a re-write of minGPT, which I think became too complicated, and which I am hesitant to now touch. Still under active development, currently working to reproduce GPT-2 on OpenWebText dataset. The code itself aims by design to be plain and readable: train.py is a ~300-line boilerplate training loop and model.py a ~300-line GPT model definition, which can optionally load the GPT-2 weights from OpenAI. That's it.

]]></description>
<dc:subject>language modeling python</dc:subject>
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<item rdf:about="https://github.com/klarrieu/RiverREM?ref=Beautiful+Public+Data-newsletter">
    <title>klarrieu/RiverREM at Beautiful Public Data-newsletter</title>
    <dc:date>2022-11-09T15:28:23+00:00</dc:date>
    <link>https://github.com/klarrieu/RiverREM?ref=Beautiful+Public+Data-newsletter</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[RiverREM is a Python package for automatically generating river relative elevation model (REM) visualizations from nothing but an input digital elevation model (DEM). The package uses the OpenStreetMap API to retrieve river centerline geometries over the DEM extent. Interpolation of river elevations is automatically handled using a sampling scheme based on raster resolution and river sinuosity to create striking high-resolution visualizations without interpolation artefacts straight out of the box and without additional manual steps.]]></description>
<dc:subject>ncgazetteer python visualization GIS</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:cc3790a69a56/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:ncgazetteer"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:visualization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:GIS"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://mathspp.com/blog/how-to-create-a-python-package-in-2022">
    <title>How to create a Python package in 2022 | Mathspp</title>
    <dc:date>2022-10-16T19:14:17+00:00</dc:date>
    <link>https://mathspp.com/blog/how-to-create-a-python-package-in-2022</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[How do you create a Python package? How do you set up automated testing and code coverage? How do you publish the package?]]></description>
<dc:subject>python packaging</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:4e6699739048/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:packaging"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://github.com/karpathy/minGPT">
    <title>karpathy/minGPT: A minimal PyTorch re-implementation of the OpenAI GPT (Generative Pretrained Transformer) training</title>
    <dc:date>2022-09-06T18:22:43+00:00</dc:date>
    <link>https://github.com/karpathy/minGPT</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[A PyTorch re-implementation of GPT, both training and inference. minGPT tries to be small, clean, interpretable and educational, as most of the currently available GPT model implementations can a bit sprawling. GPT is not a complicated model and this implementation is appropriately about 300 lines of code (see mingpt/model.py). All that's going on is that a sequence of indices feeds into a Transformer, and a probability distribution over the next index in the sequence comes out. The majority of the complexity is just being clever with batching (both across examples and over sequence length) for efficiency.]]></description>
<dc:subject>python tutorial transformer deeplearning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:09fffa5c7024/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:tutorial"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:transformer"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:deeplearning"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://til.simonwillison.net/pytest/playwright-pytest">
    <title>Using pytest and Playwright to test a JavaScript web application | Simon Willison’s TILs</title>
    <dc:date>2022-08-10T21:55:25+00:00</dc:date>
    <link>https://til.simonwillison.net/pytest/playwright-pytest</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[playwright-pytest, which lets you write tests in Python using Microsoft's Playwright browser automation library.]]></description>
<dc:subject>python web ui testing</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:c04de05142f3/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:web"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:ui"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:testing"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://nickdrozd.github.io/2022/04/12/performance-hot-spots.html">
    <title>Performance Hot Spots | Something Something Programming</title>
    <dc:date>2022-04-14T17:09:08+00:00</dc:date>
    <link>https://nickdrozd.github.io/2022/04/12/performance-hot-spots.html</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[The general principles of profiling apply to any language, but the specific instructions vary. I’ll tell you how I do it in Python. I do it the same way every time and I don’t have a great insight into how the tools work. These are the incantations that have been passed down to me.

The profiling library is called yappi.]]></description>
<dc:subject>python performance profiling</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:e00affd64785/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:performance"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:profiling"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://polyscope.run/py/">
    <title>Polyscope - Python</title>
    <dc:date>2021-09-01T15:54:32+00:00</dc:date>
    <link>https://polyscope.run/py/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Polyscope is a C++/Python viewer and user interface for 3D data such as meshes and point clouds. It allows you to register your data and quickly generate informative and beautiful visualizations, either programmatically or via a dynamic GUI.]]></description>
<dc:subject>3d c++ python</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:1bff3c03cffc/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:3d"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:c++"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:python"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://carpentries-incubator.github.io/geospatial-python/aio/index.html">
    <title>Introduction to Geospatial Raster and Vector Data with Python</title>
    <dc:date>2021-07-31T16:42:17+00:00</dc:date>
    <link>https://carpentries-incubator.github.io/geospatial-python/aio/index.html</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[This episode introduces the two primary types of geospatial data: rasters and vectors. After briefly introducing these data types, this episode focuses on raster data, describing some major features and types of raster data.]]></description>
<dc:subject>python maps tutorials geospatial ncgazetteer</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:aa0eaa57a9b5/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:maps"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:tutorials"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:geospatial"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:ncgazetteer"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://github.com/agude/wayback-machine-archiver">
    <title>agude/wayback-machine-archiver: A Python script to submit web pages to the Wayback Machine for archiving.</title>
    <dc:date>2021-05-01T19:20:29+00:00</dc:date>
    <link>https://github.com/agude/wayback-machine-archiver</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Wayback Machine Archiver (Archiver for short) is a commandline utility writen in Python to backup Github Pages using the Internet Archive.]]></description>
<dc:subject>python archive tool</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:08759c65ec07/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:archive"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:tool"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://layout-parser.github.io/">
    <title>Layout Parser</title>
    <dc:date>2021-04-09T19:59:22+00:00</dc:date>
    <link>https://layout-parser.github.io/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[With the help of state-of-the-art deep learning models, Layout Parser enables extracting complicated document structures using only several lines of code.]]></description>
<dc:subject>ocr python layout tools</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:2351f2ac5870/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:ocr"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:layout"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:tools"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://github.com/andrea-ballatore/teaching-programming-for-gis">
    <title>GitHub - andrea-ballatore/teaching-programming-for-gis: Jupyter notebooks to teach an introduction to Programming for GIS, including core Python concepts and packages to work with vector, raster, and network data.</title>
    <dc:date>2021-03-12T00:46:07+00:00</dc:date>
    <link>https://github.com/andrea-ballatore/teaching-programming-for-gis</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[This teaching material has been developed for a MSc module on programming for geographic data science at Birkbeck, University of London.

This repository contains a set of Juypter Notebooks to introduce students to the programming core concepts in Python and to packages to load, process, visualise, and analyse geospatial data.]]></description>
<dc:subject>ncgazetteer python GIS</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:c2aaa1103f54/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:ncgazetteer"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:GIS"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://github.com/Living-with-machines/DeezyMatch">
    <title>Living-with-machines/DeezyMatch: A Flexible Deep Learning Approach to Fuzzy String Matching</title>
    <dc:date>2021-01-14T17:45:45+00:00</dc:date>
    <link>https://github.com/Living-with-machines/DeezyMatch</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[DeezyMatch can be applied for performing the following tasks:

Fuzzy string matching
Record linkage
Candidate selection for entity linking systems
Toponym matching]]></description>
<dc:subject>ocr gazetteer python tools</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:60aa1c6c6e84/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:ocr"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:gazetteer"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:tools"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://bab2min.github.io/tomotopy/v0.9.0/en/">
    <title>tomotopy API documentation (v0.9.0)</title>
    <dc:date>2020-12-14T23:41:19+00:00</dc:date>
    <link>https://bab2min.github.io/tomotopy/v0.9.0/en/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[tomotopy is a Python extension of tomoto (Topic Modeling Tool) which is a Gibbs-sampling based topic model library written in C++. It utilizes a vectorization of modern CPUs for maximizing speed.]]></description>
<dc:subject>topicmodels python</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:936bbd2bc103/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:topicmodels"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:python"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.starlette.io/">
    <title>Starlette</title>
    <dc:date>2020-11-15T23:50:11+00:00</dc:date>
    <link>https://www.starlette.io/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Starlette is a lightweight ASGI framework/toolkit, which is ideal for building high performance asyncio services.

]]></description>
<dc:subject>python web framework</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:faf9865ca51a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:web"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:framework"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://typer.tiangolo.com/">
    <title>Typer</title>
    <dc:date>2020-11-15T23:49:20+00:00</dc:date>
    <link>https://typer.tiangolo.com/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Typer is a library for building CLI applications that users will love using and developers will love creating. Based on Python 3.6+ type hints.

]]></description>
<dc:subject>python cli</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:29ff08795216/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:cli"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://pydantic-docs.helpmanual.io/">
    <title>pydantic</title>
    <dc:date>2020-11-15T23:48:47+00:00</dc:date>
    <link>https://pydantic-docs.helpmanual.io/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[pydantic enforces type hints at runtime, and provides user friendly errors when data is invalid.

]]></description>
<dc:subject>python validation types</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:92312fa74438/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:validation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:types"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://fastapi.tiangolo.com/">
    <title>FastAPI</title>
    <dc:date>2020-11-15T23:48:29+00:00</dc:date>
    <link>https://fastapi.tiangolo.com/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[FastAPI is a modern, fast (high-performance), web framework for building APIs with Python 3.6+ based on standard Python type hints.]]></description>
<dc:subject>python rest api</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:5004bf13beeb/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:rest"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:api"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://github.com/ryanjgallagher/shifterator">
    <title>ryanjgallagher/shifterator: Interpretable data visualizations for understanding how texts differ at the word level</title>
    <dc:date>2020-08-22T22:29:37+00:00</dc:date>
    <link>https://github.com/ryanjgallagher/shifterator</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[The Shifterator package provides functionality for constructing word shift graphs, vertical bart charts that quantify which words contribute to a pairwise difference between two texts and how they contribute. By allowing you to look at changes in how words are used, word shifts help you to conduct analyses of sentiment, entropy, and divergence that are fundamentally more interpretable.]]></description>
<dc:subject>nlp python text visualization</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:7f52d198099a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:nlp"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:text"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:visualization"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.mkdocs.org/">
    <title>MkDocs</title>
    <dc:date>2020-02-25T21:24:43+00:00</dc:date>
    <link>https://www.mkdocs.org/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[MkDocs is a fast, simple and downright gorgeous static site generator that's geared towards building project documentation. Documentation source files are written in Markdown, and configured with a single YAML configuration file. Start by reading the introduction below, then check the User Guide for more info.]]></description>
<dc:subject>documentation markdown python tools</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:85ecee611423/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:documentation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:markdown"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:tools"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://kraken.re/">
    <title>kraken — kraken 1.0.1 documentation</title>
    <dc:date>2019-04-02T19:15:47+00:00</dc:date>
    <link>http://kraken.re/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[kraken is a turn-key OCR system forked from ocropus.]]></description>
<dc:subject>ocr python</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:992245eed61a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:ocr"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:python"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://fog.ccsf.edu/~abrick/">
    <title>CCSF // CS // BRICK</title>
    <dc:date>2018-10-24T12:28:18+00:00</dc:date>
    <link>https://fog.ccsf.edu/~abrick/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Aaron Brick's CS syllabi at CCSF.]]></description>
<dc:subject>cs syllabus programming python</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:b68879cc17e8/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:cs"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:syllabus"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:python"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://click.pocoo.org/5/">
    <title>Welcome to the Click Documentation — Click Documentation (5.0)</title>
    <dc:date>2018-04-29T01:03:03+00:00</dc:date>
    <link>http://click.pocoo.org/5/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Click is a Python package for creating beautiful command line interfaces in a composable way with as little code as necessary.]]></description>
<dc:subject>python cli</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:362be45f2d88/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:cli"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://github.com/allenai/allennlp">
    <title>allenai/allennlp: An open-source NLP research library, built on PyTorch.</title>
    <dc:date>2018-02-21T18:57:14+00:00</dc:date>
    <link>https://github.com/allenai/allennlp</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[An Apache 2.0 NLP research library, built on PyTorch, for developing state-of-the-art deep learning models on a wide variety of linguistic tasks.]]></description>
<dc:subject>nlp python</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:43ea379357f9/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:nlp"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:python"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://freediscovery.io/doc/stable/">
    <title>FreeDiscovery — FreeDiscovery 1.1.2 documentation</title>
    <dc:date>2017-06-14T19:49:21+00:00</dc:date>
    <link>http://freediscovery.io/doc/stable/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[FreeDiscovery is built on top of existing machine learning libraries (scikit-learn) and provides a REST API for information retrieval applications. It aims to benefit existing e-Discovery and information retrieval platforms with a focus on text categorization, semantic search, document clustering, duplicates detection and e-mail threading.]]></description>
<dc:subject>IR categorization clustering python</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:1b9454699775/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:IR"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:categorization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:clustering"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:python"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://course.fast.ai/">
    <title>Practical Deep Learning For Coders—18 hours of lessons for free</title>
    <dc:date>2017-02-04T18:24:44+00:00</dc:date>
    <link>http://course.fast.ai/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[fast.ai's 7 week course, Practical Deep Learning For Coders, Part 1, taught by Jeremy Howard (Kaggle's #1 competitor 2 years running, and founder of Enlitic). Learn how to build state of the art models without needing graduate-level math—but also without dumbing anything down.]]></description>
<dc:subject>machinelearning course python keras theano</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:8bfd174f01c3/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:machinelearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:course"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:keras"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:theano"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://falconframework.org/">
    <title>Falcon - The minimalist Python WSGI framework</title>
    <dc:date>2016-08-26T19:14:02+00:00</dc:date>
    <link>https://falconframework.org/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Falcon is a very fast, very minimal Python web framework for building microservices, app backends, and higher-level frameworks.

The Falcon web framework encourages the REST architectural style, meaning (among other things) that you think in terms of resources and state transitions, which map to HTTP methods.]]></description>
<dc:subject>python rest framework inls620</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:0cb03fa0b4c8/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:rest"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:framework"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:inls620"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://nbviewer.jupyter.org/github/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/blob/master/Prologue/Prologue.ipynb">
    <title>Bayesian Methods for Hackers</title>
    <dc:date>2016-04-28T19:20:31+00:00</dc:date>
    <link>http://nbviewer.jupyter.org/github/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/blob/master/Prologue/Prologue.ipynb</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Bayesian Methods for Hackers is designed as a introduction to Bayesian inference from a computational/understanding-first, and mathematics-second, point of view. Of course as an introductory book, we can only leave it at that: an introductory book. For the mathematically trained, they may cure the curiosity this text generates with other texts designed with mathematical analysis in mind. For the enthusiast with less mathematical-background, or one who is not interested in the mathematics but simply the practice of Bayesian methods, this text should be sufficient and entertaining.]]></description>
<dc:subject>python bayesian statistics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:2fb9cc2d6e72/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:bayesian"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:statistics"/>
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</item>
<item rdf:about="http://pythontutor.com/">
    <title>Python Tutor - Visualize Python, Java, JavaScript, TypeScript, Ruby, C, and C++ code execution</title>
    <dc:date>2016-03-21T02:50:18+00:00</dc:date>
    <link>http://pythontutor.com/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Python Tutor, created by Philip Guo, helps people overcome a fundamental barrier to learning programming: understanding what happens as the computer executes each line of a program's source code.

Using this tool, you can write Python, Java, JavaScript, TypeScript, Ruby, C, and C++ programs in your Web browser and visualize what the computer is doing step-by-step as it executes those programs.]]></description>
<dc:subject>python tools learning programming visualization teaching</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:9290d29889b7/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:tools"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:visualization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:teaching"/>
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</item>
<item rdf:about="http://iamtrask.github.io/2015/07/12/basic-python-network/">
    <title>A Neural Network in 11 lines of Python (Part 1) - i am trask</title>
    <dc:date>2016-03-03T22:32:47+00:00</dc:date>
    <link>http://iamtrask.github.io/2015/07/12/basic-python-network/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[This tutorial teaches backpropagation via a very simple toy example, a short python implementation.]]></description>
<dc:subject>machinelearning python tutorial</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:a4fb52019bbb/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:machinelearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:tutorial"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://keras.io/">
    <title>Keras Documentation</title>
    <dc:date>2015-12-18T16:54:36+00:00</dc:date>
    <link>http://keras.io/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Keras is a minimalist, highly modular neural networks library, written in Python and capable of running either on top of either TensorFlow or Theano. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research.

Use Keras if you need a deep learning library that: - allows for easy and fast prototyping (through total modularity, minimalism, and extensibility). - supports both convolutional networks and recurrent networks, as well as combinations of the two. - supports arbitrary connectivity schemes (including multi-input and multi-output training). - runs seamlessly on CPU and GPU.]]></description>
<dc:subject>python deeplearning library</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:23ba6315a3f5/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:deeplearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:library"/>
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</item>
<item rdf:about="https://github.com/ikreymer/pywb">
    <title>ikreymer/pywb</title>
    <dc:date>2015-12-11T19:36:43+00:00</dc:date>
    <link>https://github.com/ikreymer/pywb</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[pywb is a python implementation of web archival replay tools, sometimes also known as 'Wayback Machine'.

pywb allows high-quality replay (browsing) of archived web data stored in standardized ARC and WARC, and it can also serve as a customizable rewriting proxy to live web content.

The replay system is designed to accurately replay complex dynamic sites, including video and audio content and sites with complex JavaScript.

Additionally, pywb includes an extensive index query api for querying information about archived content.

The software can run as a traditional web application or an HTTP or HTTPS proxy server, and has been tested on Linux, OS X and Windows platforms.

pywb is fully compliant with the Memento protocol (RFC-7089).]]></description>
<dc:subject>python web archive tools</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:26eda6502405/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:web"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:archive"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:tools"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.umiacs.umd.edu/~hal/sayit.py">
    <title>sayit.py</title>
    <dc:date>2015-09-12T01:14:11+00:00</dc:date>
    <link>http://www.umiacs.umd.edu/~hal/sayit.py</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[“Given a string of text in some language you might want to know how long it would take to speak it.”]]></description>
<dc:subject>nlp speech tools python</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:cd06d9a368a9/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:nlp"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:speech"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:tools"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:python"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://github.com/recipy/recipy">
    <title>recipy/recipy</title>
    <dc:date>2015-08-27T23:24:20+00:00</dc:date>
    <link>https://github.com/recipy/recipy</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[With the addition of a single line of code to the top of your Python files, ReciPy will log each run of your code to a database, keeping track of the input files, output files and the version of your code.]]></description>
<dc:subject>python provenance research metadata management tools</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:8b4c39660da2/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:provenance"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:research"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:metadata"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:management"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:tools"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://honnibal.github.io/spaCy/">
    <title>spaCy: Industrial-strength NLP — spaCy 0.85 documentation</title>
    <dc:date>2015-06-20T23:53:42+00:00</dc:date>
    <link>http://honnibal.github.io/spaCy/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[spaCy’s parser is faster than most taggers, and its tokenizer is fast enough for any workload. And the tokenizer doesn’t just give you a list of strings. A spaCy token is a pointer to a Lexeme struct, from which you can access a wide range of pre-computed features, including embedded word representations.]]></description>
<dc:subject>python nlp</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:7118b1578358/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:nlp"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://github.com/bmabey/pyLDAvis/blob/master/README.rst">
    <title>pyLDAvis/README.rst at master · bmabey/pyLDAvis</title>
    <dc:date>2015-06-08T13:28:16+00:00</dc:date>
    <link>https://github.com/bmabey/pyLDAvis/blob/master/README.rst</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[pyLDAvis is designed to help users interpret the topics in a topic model that has been fit to a corpus of text data. The package extracts information from a fitted LDA topic model to inform an interactive web-based visualization.

The visualization is intended to be used within an IPython notebook but can also be saved to a stand-alone HTML file for easy sharing.]]></description>
<dc:subject>lda visualization python topicmodels</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:98f0e6bdeaf5/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:lda"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:visualization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:topicmodels"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://github.com/jakevdp/sklearn_pycon2015/">
    <title>jakevdp/sklearn_pycon2015</title>
    <dc:date>2015-04-15T23:14:56+00:00</dc:date>
    <link>https://github.com/jakevdp/sklearn_pycon2015/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[an introduction to the core concepts of machine learning and the Scikit-Learn package. We will introduce the scikit-learn API, and use it to explore the basic categories of machine learning problems and related topics such as feature selection and model validation, and practice applying these tools to real-world data sets.]]></description>
<dc:subject>python machinelearning tutorial</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:e350100eba74/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:machinelearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:tutorial"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.markhneedham.com/blog/2015/03/22/python-simplifying-the-creation-of-a-stop-word-list-with-defaultdict/">
    <title>Python: Simplifying the creation of a stop word list with defaultdict at Mark Needham</title>
    <dc:date>2015-03-23T13:49:17+00:00</dc:date>
    <link>http://www.markhneedham.com/blog/2015/03/22/python-simplifying-the-creation-of-a-stop-word-list-with-defaultdict/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[A good heuristic for identifying such words is to remove those that occur in more than 5-10% of documents (most common) and those that occur fewer than 5-10 times in the entire corpus (least common).]]></description>
<dc:subject>textanalysis howto python</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:f56aab9c2e89/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:textanalysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:howto"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:python"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://github.com/StanfordHCI/termite/blob/master/pipeline/compute_similarity.py">
    <title>termite/compute_similarity.py at master · StanfordHCI/termite</title>
    <dc:date>2015-02-14T17:46:20+00:00</dc:date>
    <link>https://github.com/StanfordHCI/termite/blob/master/pipeline/compute_similarity.py</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Compute term similarity based on co-occurrence and collocation likelihoods.]]></description>
<dc:subject>python nlp similarity</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:3438bb9d7470/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:nlp"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:similarity"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://jordan-wright.github.io/blog/2014/10/06/creating-tor-hidden-services-with-python/">
    <title>Creating Tor Hidden Services with Python - jordan-wright</title>
    <dc:date>2014-12-23T13:28:24+00:00</dc:date>
    <link>https://jordan-wright.github.io/blog/2014/10/06/creating-tor-hidden-services-with-python/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Tor is often used to protect the anonymity of someone who is trying to connect to a service. However, it is also possible to use Tor to protect the anonymity of a service provider via hidden services. These services, operating under the .onion TLD, allow publishers to anonymously create and host content viewable only by other Tor users.

The Tor project has instructions on how to create hidden services, but this can be a manual and arduous process if you want to setup multiple services. This post will show how we can use the fantastic stem Python library to automatically create and host a Tor hidden service.]]></description>
<dc:subject>python anonymity tor howto privacy</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:80005cccf66f/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:anonymity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:tor"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:howto"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:privacy"/>
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</item>
<item rdf:about="https://github.com/andersbll/deeppy">
    <title>andersbll/deeppy</title>
    <dc:date>2014-12-20T16:58:11+00:00</dc:date>
    <link>https://github.com/andersbll/deeppy</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[DeepPy tries to combine state-of-the-art deep learning models with a Pythonic interface in an extensible framework.]]></description>
<dc:subject>python deeplearning machinelearning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:5f6758a42906/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:deeplearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:machinelearning"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://github.com/minerva-developers/minerva">
    <title>minerva-developers/minerva</title>
    <dc:date>2014-12-18T13:07:54+00:00</dc:date>
    <link>https://github.com/minerva-developers/minerva</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Minerva: a fast and flexible tool for deep learning. It provides ndarray programming interface, just like Numpy. Python bindings and C++ bindings are both available. The resulting code can be run on CPU or GPU. Multi-GPU support is very easy.]]></description>
<dc:subject>deeplearning python machinelearning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:f57756425ce8/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:deeplearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:machinelearning"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://weasyprint.org/">
    <title>WeasyPrint - Converts HTML + CSS to PDF - WeasyPrint converts HTML/CSS documents to PDF</title>
    <dc:date>2014-01-14T19:29:35+00:00</dc:date>
    <link>http://weasyprint.org/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[WeasyPrint is a visual rendering engine for HTML and CSS that can export to PDF. It aims to support web standards for printing.]]></description>
<dc:subject>html css pdf python</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:ec0547593e8b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:html"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:css"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:pdf"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:python"/>
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</item>
<item rdf:about="https://github.com/yinwang0/pysonar2">
    <title>yinwang0/pysonar2</title>
    <dc:date>2013-11-19T02:05:35+00:00</dc:date>
    <link>https://github.com/yinwang0/pysonar2</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[PySonar2 is a static analyzer for Python, which does sophisticated interprocedural analysis to infer types.]]></description>
<dc:subject>python analysis development tools debugging</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:d2bac7c11638/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:development"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:tools"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:debugging"/>
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<item rdf:about="https://github.com/ariddell/horizont">
    <title>ariddell/horizont</title>
    <dc:date>2013-11-11T21:03:38+00:00</dc:date>
    <link>https://github.com/ariddell/horizont</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[horizont implements a number of topic models. Conventions from scikit-learn are followed.

The project is focused on making accessible a variety of models for researchers in the human and social sciences.]]></description>
<dc:subject>python topicmodels lda code</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:ecd74a2626ee/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:topicmodels"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:lda"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:code"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://nbviewer.ipython.org/urls/raw.github.com/agconti/kaggle-titanic/master/Titanic.ipynb">
    <title>Titanic Machine Learning from Disaster</title>
    <dc:date>2013-10-15T19:24:48+00:00</dc:date>
    <link>http://nbviewer.ipython.org/urls/raw.github.com/agconti/kaggle-titanic/master/Titanic.ipynb</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Shows a simple example of an analysis of the Titanic disaster in Python using a full complement of PyData utilities. This is aimed for those looking to get into the field or those who are already in the field and looking to see an example of an analysis done with Python.]]></description>
<dc:subject>python statistics pydata ipython</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:4b99519c0777/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:pydata"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:ipython"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://elasticutils.readthedocs.org/en/latest/">
    <title>ElasticUtils — ElasticUtils 0.9.dev documentation</title>
    <dc:date>2013-09-11T17:45:10+00:00</dc:date>
    <link>http://elasticutils.readthedocs.org/en/latest/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[ElasticUtils is a Python library that gives you a chainable search API for Elasticsearch as well as some other tools to make it easier to integrate Elasticsearch into your application.]]></description>
<dc:subject>python elasticsearch</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:1437cb3eeccf/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:elasticsearch"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://dahlia.kr/pghstore/">
    <title>pghstore — PostgreSQL hstore formatter — pghstore 1.0.0 documentation</title>
    <dc:date>2013-06-05T12:25:54+00:00</dc:date>
    <link>http://dahlia.kr/pghstore/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[This small module implements a formatter and a loader for hstore, one of PostgreSQL supplied modules, that stores simple key-value pairs.]]></description>
<dc:subject>postgresql python database nosql</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:3ad2a4f1db74/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:postgresql"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:database"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:nosql"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://gist.github.com/FND/5636996">
    <title>wsgi-intercept vs. Requests / urllib3</title>
    <dc:date>2013-05-28T16:18:48+00:00</dc:date>
    <link>https://gist.github.com/FND/5636996</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[wsgi-intercept vs. Requests / urllib3]]></description>
<dc:subject>python http testing</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:43f562b4a0a0/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:http"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:testing"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://github.com/RDFLib/rdflib/blob/master/examples/conjunctive_graphs.py">
    <title>rdflib/examples/conjunctive_graphs.py at master · RDFLib/rdflib · GitHub</title>
    <dc:date>2013-05-24T17:27:56+00:00</dc:date>
    <link>https://github.com/RDFLib/rdflib/blob/master/examples/conjunctive_graphs.py</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[An RDFLib ConjunctiveGraph is an (unamed) aggregation of all the named graphs
within a Store. The :meth:`~rdflib.graph.ConjunctiveGraph.get_context`
method can be used to get a particular named graph, or triples can be
added to the default graph.]]></description>
<dc:subject>python rdflib</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:213c23660646/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:rdflib"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://dev.w3.org/2004/PythonLib-IH/Doc-pyRdfa/">
    <title>pyRdfa API Documentation</title>
    <dc:date>2013-05-08T20:06:02+00:00</dc:date>
    <link>http://dev.w3.org/2004/PythonLib-IH/Doc-pyRdfa/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[RDFa 1.1 parser, also referred to as a “RDFa Distiller”. It is deployed, via a CGI front-end, on the W3C RDFa 1.1 Distiller page.]]></description>
<dc:subject>rdfa python documentation</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:2635ca4a67e1/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:rdfa"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:documentation"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://sagemath.org/">
    <title>Sage: Open Source Mathematics Software</title>
    <dc:date>2013-04-28T23:58:13+00:00</dc:date>
    <link>http://sagemath.org/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Sage is a free open-source mathematics software system licensed under the GPL. It combines the power of many existing open-source packages into a common Python-based interface.
Mission: Creating a viable free open source alternative to Magma, Maple, Mathematica and Matlab.]]></description>
<dc:subject>python math science matlab</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:846f221a7a19/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:math"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:matlab"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://github.com/scrapy/scrapely">
    <title>scrapy/scrapely · GitHub</title>
    <dc:date>2013-04-10T21:50:17+00:00</dc:date>
    <link>https://github.com/scrapy/scrapely</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Scrapely is a library for extracting structured data from HTML pages. Given some example web pages and the data to be extracted, scrapely constructs a parser for all similar pages.]]></description>
<dc:subject>python web scraping</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:b59a8b6cb084/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:web"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:scraping"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://guidetodatamining.com/">
    <title>A Programmer's Guide to Data Mining | The Ancient Art of the Numerati</title>
    <dc:date>2013-04-06T15:13:53+00:00</dc:date>
    <link>http://guidetodatamining.com/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Before you is a tool for learning basic data mining techniques. Most data mining textbooks focus on providing a theoretical foundation for data mining, and as result, may seem notoriously difficult to understand. Don’t get me wrong, the information in those books is extremely important. However, if you are a programmer interested in learning a bit about data mining you might be interested in a beginner’s hands-on guide as a first step. That’s what this book provides.]]></description>
<dc:subject>datamining machinelearning python book</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:0ec40ed11e7e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:datamining"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:machinelearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:book"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://code.google.com/p/pyrtf-ng/">
    <title>pyrtf-ng - Next Generation Python RTF Generation and Parsing - Google Project Hosting</title>
    <dc:date>2013-04-05T15:29:31+00:00</dc:date>
    <link>http://code.google.com/p/pyrtf-ng/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[A fork of PyRTF, pyrtf-ng aims to be more efficient and adhere to the subset of PEP-8 standard as dictated by the Twisted coding standard, provide Rich Text Format file parsing, while still enabling users to generate RTF files from Python.]]></description>
<dc:subject>python documents</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:544594c9fe18/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:documents"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers">
    <title>CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers · GitHub</title>
    <dc:date>2013-04-02T21:14:36+00:00</dc:date>
    <link>https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[An introduction to Bayesian methods + probabilistic programming in data analysis with a computation/understanding-first, mathematics-second point of view. All in pure Python ;)]]></description>
<dc:subject>python statistics bayesian</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:330cabc877d8/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:bayesian"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://pypi.python.org/pypi/Unidecode">
    <title>Unidecode 0.04.12 : Python Package Index</title>
    <dc:date>2013-03-01T15:20:10+00:00</dc:date>
    <link>https://pypi.python.org/pypi/Unidecode</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[It often happens that you have text data in Unicode, but you need to represent it in ASCII. For example when integrating with legacy code that doesn't support Unicode, or for ease of entry of non-Roman names on a US keyboard, or when constructing ASCII machine identifiers from human-readable Unicode strings that should still be somewhat intelligeble (a popular example of this is when making an URL slug from an article title).

In most of these examples you could represent Unicode characters as "???" or "15BA15A01610", to mention two extreme cases. But that's nearly useless to someone who actually wants to read what the text says.

What Unidecode provides is a middle road: function unidecode() takes Unicode data and tries to represent it in ASCII characters (i.e., the universally displayable characters between 0x00 and 0x7F), where the compromises taken when mapping between two character sets are chosen to be near what a human with a US keyboard would choose.]]></description>
<dc:subject>python unicode encoding</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:485f05999b47/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:unicode"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:encoding"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/pdf/1301.7738v1.pdf">
    <title>PyPLN: a Distributed Platform for Natural Language Processing</title>
    <dc:date>2013-02-10T16:32:29+00:00</dc:date>
    <link>http://arxiv.org/pdf/1301.7738v1.pdf</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[This paper presents a distributed platform for Natural Language Processing called PyPLN. PyPLN leverages a vast array of NLP and text processing open source tools, managing the distribution of the workload on a variety of configurations: from a single server to a cluster of linux servers. PyPLN is developed using Python 2.7.3 but makes it very easy to incorporate other softwares for specific tasks as long as a linux version is available. PyPLN facilitates analyses both at document and corpus level, simplifying management and publication of corpora and analytical results through an easy to use web interface. In the current (beta) release, it supports English and Portuguese languages with support to other languages planned for future releases. To support the Portuguese language PyPLN uses the PALAVRAS parsercitep{Bick2000}. Currently PyPLN offers the following features: Text extraction with encoding normalization (to UTF-8), part-of-speech tagging, token frequency, semantic annotation, n-gram extraction, word and sentence repertoire, and full-text search across corpora. The platform is licensed as GPL-v3.]]></description>
<dc:subject>python nlp tools</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:8c89fed33489/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:nlp"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:tools"/>
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