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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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    <dc:date>2024-01-21T01:15:24+00:00</dc:date>
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    <link>https://developer.mozilla.org/en-US/docs/Web/API/Web_components</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Web Components is a suite of different technologies allowing you to create reusable custom elements — with their functionality encapsulated away from the rest of your code — and utilize them in your web apps.

]]></description>
<dc:subject>web ui components documentation tutorial</dc:subject>
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    <title>hackerllama - The Random Transformer</title>
    <dc:date>2024-01-11T20:13:51+00:00</dc:date>
    <link>https://osanseviero.github.io/hackerllama/blog/posts/random_transformer/#conclusions</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[An end-to-end example of the math within a transformer model. The goal is to get a good understanding of how the model works. To make this manageable, we’ll do lots of simplification. As we’ll be doing quite a bit of the math by hand, we’ll reduce the dimensions of the model. For example, rather than using embeddings of 512 values, we’ll use embeddings of 4 values. This will make the math easier to follow! We’ll use random vectors and matrices, but you can use your own values if you want to follow along.

As you’ll see, the math is not that complicated. The complexity comes from the number of steps and the number of parameters. I recommend you to read the The Illustrated Transformer blog before reading this blog post (or reading in parallel). It’s a great blog post that explains the transformer model in a very intuitive (and illustrative!) way and I don’t intend to explain what it’s already explained there. My goal is to explain the “how” of the transformer model, not the “what”. If you want to dive even deeper, check out the famous original paper: Attention is all you need.]]></description>
<dc:subject>transformer tutorial</dc:subject>
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    <dc:date>2023-02-18T00:33:06+00:00</dc:date>
    <link>https://exercism.org/tracks/elixir</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Join Exercism’s Elixir Track for access to 156 exercises grouped into 57 Elixir Concepts, with automatic analysis of your code and personal mentoring, all 100% free.

]]></description>
<dc:subject>elixir courses tutorial programming</dc:subject>
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    <title>Divio Documentation System</title>
    <dc:date>2023-01-30T19:51:43+00:00</dc:date>
    <link>https://documentation.divio.com/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[There is a secret that needs to be understood in order to write good software documentation: there isn’t one thing called documentation, there are four.

They are: tutorials, how-to guides, technical reference and explanation. They represent four different purposes or functions, and require four different approaches to their creation. Understanding the implications of this will help improve most documentation - often immensely.]]></description>
<dc:subject>documentation howto reference tutorial</dc:subject>
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<item rdf:about="https://uvadlc-notebooks.readthedocs.io/en/latest/tutorial_notebooks/tutorial2/Introduction_to_PyTorch.html">
    <title>Tutorial 2: Introduction to PyTorch — UvA DL Notebooks v1.2 documentation</title>
    <dc:date>2022-11-23T22:24:06+00:00</dc:date>
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    <dc:creator>rybesh</dc:creator><description><![CDATA[Welcome to our PyTorch tutorial for the Deep Learning course 2022 at the University of Amsterdam! The following notebook is meant to give a short introduction to PyTorch basics, and get you setup for writing your own neural networks.]]></description>
<dc:subject>pytorch tutorial machinelearning</dc:subject>
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    <title>Grokking Stable Diffusion.ipynb - Colaboratory</title>
    <dc:date>2022-09-06T19:43:57+00:00</dc:date>
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    <dc:creator>rybesh</dc:creator><description><![CDATA[I've been playing with stable diffusion (who hasn't) and starting to think about how to structure learning material about diffusion models. This notebook is basically a tidier version of my first dabble with the model, trying to understand ('grok') the individual parts and to think about how they could be presented as part of a lesson.]]></description>
<dc:subject>stablediffusion tutorial</dc:subject>
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    <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>
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    <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>
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    <title>A Gentle Introduction to Graph Neural Networks</title>
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    <link>https://distill.pub/2021/gnn-intro/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Neural networks have been adapted to leverage the structure and properties of graphs. We explore the components needed for building a graph neural network - and motivate the design choices behind them.]]></description>
<dc:subject>graphs machinelearning tutorial neural</dc:subject>
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    <title>500 Lines or Less | Optical Character Recognition (OCR)</title>
    <dc:date>2021-11-17T22:47:45+00:00</dc:date>
    <link>http://aosabook.org/en/500L/optical-character-recognition-ocr.html</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Our OCR system will consist of 5 main components, divided into 5 files. There will be:

a client (ocr.js)
a server (server.py)
a simple user interface (ocr.html)
an ANN trained via backpropagation (ocr.py)
an ANN design script (neural_network_design.py)
The user interface will be simple: a canvas to draw digits on and buttons to either train the ANN or request a prediction. The client will gather the drawn digit, translate it into an array, and pass it to the server to be processed either as a training sample or as a prediction request. The server will simply route the training or prediction request by making API calls to the ANN module. The ANN module will train the network with an existing data set on its first initialization. It will then save the ANN weights to a file and re-load them on subsequent startups. This module is where the core of training and prediction logic happens. Finally, the design script is for experimenting with different hidden node counts and deciding what works best. Together, these pieces give us a very simplistic, but functional OCR system.]]></description>
<dc:subject>ANN neuralnetwork backprop tutorial</dc:subject>
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<item rdf:about="https://www.learnwikidata.net/">
    <title>Learn Wikidata</title>
    <dc:date>2021-11-03T17:44:01+00:00</dc:date>
    <link>https://www.learnwikidata.net/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[A course for information professionals developed at Vanderbilt.]]></description>
<dc:subject>wikidata tutorial ncgazetteer</dc:subject>
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    <title>Open Annotation Draft Data Model</title>
    <dc:date>2021-02-22T18:48:12+00:00</dc:date>
    <link>http://www.commonsemantics.com/oa/Open%20Annotation%20Data%20Model%20Primer.html</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[This document provides an intuitive introduction and guide to the Open Annotation Data Model [OA-DM], an interoperable framework for creating associations between related resources, annotations, using a methodology which conforms to the Architecture of the World Wide Web. This primer explains the fundamental Open Annotation Data Model concepts and provides examples of its use. The primer is intended as a starting point for those wishing to create or use Open Annotation Data Model compliant annotation data.

]]></description>
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	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:standards"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:tutorial"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:linkeddata"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://medium.com/@mbostock/command-line-cartography-part-1-897aa8f8ca2c">
    <title>Command-Line Cartography, Part 1 - Mike Bostock - Medium</title>
    <dc:date>2020-05-12T17:49:09+00:00</dc:date>
    <link>https://medium.com/@mbostock/command-line-cartography-part-1-897aa8f8ca2c</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[This multipart tutorial will teach you to make a thematic map from the command line using d3-geo, TopoJSON and ndjson-cli—free, open-source tools written in JavaScript. We’ll make a choropleth of California’s population density.]]></description>
<dc:subject>d3 maps tutorial</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:76dd6545526f/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:d3"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:maps"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:tutorial"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://coral.ai/projects/teachable-sorter/">
    <title>Teachable Sorter | Coral</title>
    <dc:date>2019-11-24T21:28:57+00:00</dc:date>
    <link>https://coral.ai/projects/teachable-sorter/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[A machine that you can teach to rapidly recognize and sort objects using your own custom machine learning models.]]></description>
<dc:subject>image recognition project tutorial</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:c4ff8d9e6f0d/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:image"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:recognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:project"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:tutorial"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://coral.ai/projects/bird-feeder/">
    <title>Smart Bird Feeder | Coral</title>
    <dc:date>2019-11-24T21:28:13+00:00</dc:date>
    <link>https://coral.ai/projects/bird-feeder/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[A smart bird feeder that uses an image classification model to identify birds, record animal visits, and deter squirrels from stealing bird seed.]]></description>
<dc:subject>image recognition project tutorial</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:e88e2d15634a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:image"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:recognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:project"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:tutorial"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://noffle.github.io/kappa-arch-workshop/build/01.html">
    <title>Problem 01</title>
    <dc:date>2019-02-27T14:52:18+00:00</dc:date>
    <link>https://noffle.github.io/kappa-arch-workshop/build/01.html</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Today we are gonna explore building collaborative peer-to-peer applications. We are gonna go through a lot of different interesting concepts around data structures, data views, networking, and much more.]]></description>
<dc:subject>tutorial p2p javascript decentralization</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:b80a62920ad0/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:tutorial"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:p2p"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:javascript"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:decentralization"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://vallandingham.me/regl_intro.html">
    <title>An Intro to regl for Data Visualization - Jim Vallandingham</title>
    <dc:date>2019-02-13T16:48:50+00:00</dc:date>
    <link>https://vallandingham.me/regl_intro.html</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[regl is a technology meant to simplify building awesome things with WebGL. Recently, Mikola Lysenko, one of the creators of regl, gave a great talk at OpenVis Conf that got me wanting to spend more time with WebGL and regl in particular - to see how it could be applied to my data visualization work.

With this tutorial, I hope to share my brief learnings on this wonderfully mystical technology and remove some of that magic.]]></description>
<dc:subject>infoviz webgl 3d howto tutorial</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:9a36b36a94de/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:infoviz"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:webgl"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:3d"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:howto"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:tutorial"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://course.fast.ai/lessons/lesson1.html">
    <title>Deep Learning For Coders—36 hours of lessons for free</title>
    <dc:date>2018-10-21T20:20:40+00:00</dc:date>
    <link>http://course.fast.ai/lessons/lesson1.html</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Welcome to the start of your fast.ai journey! In today’s lesson you’ll set up your deep learning server, and train your first image classification model (a convolutional neural network, or CNN), which will learn to distinguish dogs from cats nearly perfectly. If you need help at any time, head over to forums.fast.ai where over a thousand students are discussing the course and have provided lots of tips and tricks for you.

Each lesson page includes a link to a forum topic that includes a hyperlinked timeline, links to further resources, and a discussion of the lesson. Nearly all the participants in the original in-person course said that they found these resources very important for successfully completing the course. So be sure to make the most of them! And be sure to carefully read the Getting Started page to find out how this course is designed and how to get the most out of it.]]></description>
<dc:subject>deeplearning tutorial course</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:895deb6e948e/</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:tutorial"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:course"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.youtube.com/playlist?list=PLoRl3Ht4JOcdU872GhiYWf6jwrk_SNhz9">
    <title>[Coursera] Neural Networks for Machine Learning — Geoffrey Hinton 2016 - YouTube</title>
    <dc:date>2017-04-12T12:24:47+00:00</dc:date>
    <link>https://www.youtube.com/playlist?list=PLoRl3Ht4JOcdU872GhiYWf6jwrk_SNhz9</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Learn about artificial neural networks and how they're being used for machine learning, as applied to speech and object recognition, image segmentation, modeling language and human motion, etc. We'll emphasize both the basic algorithms and the practical tricks needed to get them to work well.

As taught by Prof. Geoffrey Hinton on Coursera in 2012.]]></description>
<dc:subject>deeplearning tutorial</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:0ede598fbcc4/</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:tutorial"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://viewsourcecode.org/snaptoken/kilo/">
    <title>Table of contents | Build Your Own Text Editor</title>
    <dc:date>2017-04-06T15:55:22+00:00</dc:date>
    <link>http://viewsourcecode.org/snaptoken/kilo/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Welcome! This is an instruction booklet that shows you how to build a text editor in C.
The text editor is antirez’s kilo, with some changes. It’s about 1000 lines of C in a single file with no dependencies, and it implements all the basic features you expect in a minimal editor, as well as syntax highlighting and a search feature.
This booklet walks you through building the editor in 184 steps. Each step, you’ll add, change, or remove a few lines of code. Most steps, you’ll be able to observe the changes you made by compiling and running the program immediately afterwards.]]></description>
<dc:subject>c tutorial editing</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:69bbe0591f9b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:c"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:tutorial"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:editing"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://ec.haxx.se/">
    <title>Introduction · Everything curl</title>
    <dc:date>2017-02-27T13:22:41+00:00</dc:date>
    <link>https://ec.haxx.se/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Everything curl is an extensive guide to everything there is to know about curl, the project, the command-line tool, the library, how everything started and how it came to be what it is today.]]></description>
<dc:subject>curl http tutorial inls620</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:f62d9728dae2/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:curl"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:http"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:tutorial"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:inls620"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://github.com/jsonlines/guide">
    <title>jsonlines/guide: Tutorial on streaming JSON data analysis on the command line</title>
    <dc:date>2017-02-04T18:10:27+00:00</dc:date>
    <link>https://github.com/jsonlines/guide</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Tutorial on streaming JSON data analysis on the command line.]]></description>
<dc:subject>json data analysis tutorial</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:f328912df653/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:json"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:tutorial"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://programminghistorian.org/lessons/json-and-jq">
    <title>Reshaping JSON with jq | Programming Historian</title>
    <dc:date>2016-09-06T19:25:19+00:00</dc:date>
    <link>http://programminghistorian.org/lessons/json-and-jq</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Working with data from an art museum API and from the Twitter API, this lesson teaches how to use the command-line utility jq to filter and parse complex JSON files into flat CSV files. This lesson will begin with an overview of the basic operators of the jq query syntax. Next, you will learn progressively more complex ways of connecting these operators together. By the end of the lesson, you will understand how to combine basic operators to create queries that can reshape many types of JSON data.]]></description>
<dc:subject>json tutorial inls520 inls620</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:32eb0cadf9d8/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:json"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:tutorial"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:inls520"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:inls620"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://jgthms.com/web-design-in-4-minutes/">
    <title>Web Design in 4 minutes</title>
    <dc:date>2016-08-04T00:21:43+00:00</dc:date>
    <link>http://jgthms.com/web-design-in-4-minutes/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Let's say you have a product, a portfolio, or just an idea you want to share with everyone on your own website. Before you publish it on the internet, you want to make it look attractive, professional, or at least decent to look at.

What is the first thing you need to work on?]]></description>
<dc:subject>css tutorial</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:34c8d66c5b31/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:css"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:tutorial"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://ia.net/writer/updates/video-tutorials">
    <title>iA Writer Video Tutorials | iA</title>
    <dc:date>2016-04-28T19:01:55+00:00</dc:date>
    <link>https://ia.net/writer/updates/video-tutorials</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[If you are not familiar with Markdown, it might look a little scary at first. Once you get the basics, you will quickly love it as it allows you to format your text without taking your hands off the keyboard. Check out the videos here to learn in no time at all how easy Markdown can be.]]></description>
<dc:subject>markdown tutorial video</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:eceb99f02dbe/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:markdown"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:tutorial"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:video"/>
</rdf:Bag></taxo:topics>
</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="https://www.tensorflow.org/versions/master/tutorials/mnist/beginners/index.html#mnist-for-ml-beginners">
    <title>TensorFlow -- MNIST For ML Beginners</title>
    <dc:date>2016-01-13T13:58:25+00:00</dc:date>
    <link>https://www.tensorflow.org/versions/master/tutorials/mnist/beginners/index.html#mnist-for-ml-beginners</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[In this tutorial, we're going to train a model to look at images and predict what digits they are. Our goal isn't to train a really elaborate model that achieves state-of-the-art performance -- although we'll give you code to do that later! -- but rather to dip a toe into using TensorFlow. As such, we're going to start with a very simple model, called a Softmax Regression.

The actual code for this tutorial is very short, and all the interesting stuff happens in just three lines. However, it is very important to understand the ideas behind it: both how TensorFlow works and the core machine learning concepts. Because of this, we are going to very carefully work through the code.]]></description>
<dc:subject>machinelearning tensorflow tutorial</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:21f1f8943d7e/</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:tensorflow"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:tutorial"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://yogthos.github.io/ClojureDistilled.html">
    <title>Clojure Distilled</title>
    <dc:date>2016-01-07T13:32:06+00:00</dc:date>
    <link>https://yogthos.github.io/ClojureDistilled.html</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[The goal of this guide is to provide an overview of the core concepts necessary to become productive with Clojure.]]></description>
<dc:subject>clojure tutorial</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:fab262fb08d3/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:clojure"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:tutorial"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.tensorflow.org/versions/master/tutorials/recurrent/index.html">
    <title>Language Modeling with Recurrent Neural Networks</title>
    <dc:date>2015-12-09T20:02:47+00:00</dc:date>
    <link>https://www.tensorflow.org/versions/master/tutorials/recurrent/index.html</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[In this tutorial we will show how to train a recurrent neural network on a challenging task of language modeling. The goal of the problem is to fit a probabilistic model which assigns probablities to sentences. It does so by predicting next words in a text given a history of previous words. For this purpose we will use the Penn Tree Bank (PTB) dataset, which is a popular benchmark for measuring quality of these models, whilst being small and relatively fast to train.]]></description>
<dc:subject>deeplearning tutorial datastudies</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:03e249391f7e/</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:tutorial"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:datastudies"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.fpcomplete.com/blog/2015/08/new-in-depth-guide-stack">
    <title>New in-depth guide to stack | FP Complete</title>
    <dc:date>2015-08-31T12:50:38+00:00</dc:date>
    <link>https://www.fpcomplete.com/blog/2015/08/new-in-depth-guide-stack</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[stack is a cross-platform program for developing Haskell projects. This guide is intended to step a new stack user through all of the typical stack workflows.]]></description>
<dc:subject>haskell development tools tutorial</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:31833b1a5b63/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:haskell"/>
	<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:tutorial"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.youtube.com/watch?v=6sBL1kHoMoo">
    <title>Thinking like an Erlanger - Torben Hoffmann - YouTube</title>
    <dc:date>2015-08-20T12:17:14+00:00</dc:date>
    <link>https://www.youtube.com/watch?v=6sBL1kHoMoo</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[If you find Erlang is a bit tough, or if testing gives you headaches, this webinar is for you. We will spend most of this intensive session looking at how to design systems with asynchronous message passing between processes that do not share any memory.

Since processes are so cheap to create in the Erlang run-time, one has to embrace designing with lots and lots of processes, which puts an emphasis on the interaction between the processes. For this we will look at Message Sequence Charts (MSCs) and enjoy how the protocols we designed are easy to identify in the code.

As testing asynchronous message passing can be a bit tricky, you will get a short intro on how to do that with QuickCheck's mocking facilities, and touch briefly on failure handling and how that influences the design of Erlang programs.

The running example is Conway's Game of Life, which fits Erlang really well and poses some interesting problems in the implementation, that apply to all asynchronous message passing solutions.]]></description>
<dc:subject>erlang distributed programming tutorial</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:79320c901f8b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:erlang"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:distributed"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:tutorial"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://seanhess.github.io/2015/08/04/practical-haskell-getting-started.html">
    <title>Practical Haskell - Getting Started</title>
    <dc:date>2015-08-18T20:14:34+00:00</dc:date>
    <link>http://seanhess.github.io/2015/08/04/practical-haskell-getting-started.html</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Haskell is famous for having a steep learning curve. As a web developer we’re used to clear tutorials that we can understand and complete within an hour or two. Haskell introduces many new concepts not found in other languages, but we can learn faster by spending as much time coding as we do reading.

This is the first of a tutorial series intended to introduce Haskell by coding things that work.]]></description>
<dc:subject>haskell tutorial</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:4e84e77edd66/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:haskell"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:tutorial"/>
</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.oliverelliott.org/article/computing/tut_unix/">
    <title>Oliver | An Introduction to Unix</title>
    <dc:date>2015-03-05T19:29:24+00:00</dc:date>
    <link>http://www.oliverelliott.org/article/computing/tut_unix/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Everybody Knows How to Use a Computer, but Not Everyone Knows How to Use the Command Line. Yet This is the Gateway to Doing Anything and Everything Sophisticated with a Computer and the Most Natural Starting Place to Learn Programming ]]></description>
<dc:subject>unix tutorial</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:817867b7b465/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:unix"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:tutorial"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://fr.umio.us/why-ramda/#comment-1433967883">
    <title>Why Ramda?</title>
    <dc:date>2014-06-16T16:51:21+00:00</dc:date>
    <link>http://fr.umio.us/why-ramda/#comment-1433967883</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[To those not used to functional programming, Ramda seems to serve no purpose whatsoever. Most of its major capabilities are already covered by libraries like Underscore and LoDash.

These folks are right. If you want to keep coding with the same imperative and object-oriented styles you've been using, Ramda does not have much to offer you.

However, it does offer a different style of coding, a style that's taken for granted in purely functional programming languages: Ramda makes it simple for you to build complex logic through functional composition. Note that any library with a compose function will allow you do functional composition; the real point here is: "makes it simple".]]></description>
<dc:subject>javascript functional tutorial</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:c568b7432acd/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:javascript"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:functional"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:tutorial"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://clojurekoans.com/">
    <title>Clojure Koans</title>
    <dc:date>2014-04-28T02:52:25+00:00</dc:date>
    <link>http://clojurekoans.com/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Clojure koans are exercises meant to initiate you to the mysteries of the Clojure language. By following along the path set before you, you will touch on all the major aspects of the language, from simple datatypes to macros, from tail recursion to Java interoperability.]]></description>
<dc:subject>clojure tutorial</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:5b3744f7a2dc/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:clojure"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:tutorial"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://nodeschool.io/">
    <title>nodeschool.io</title>
    <dc:date>2014-04-15T13:02:21+00:00</dc:date>
    <link>http://nodeschool.io/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Install these choose-your-own-adventure style lessons and learn how to use node.js, npm and other related tools by writing code to solve realistic problems. The lessons run in your terminal and work on Windows, Mac and Linux. Select a lesson below to get started!]]></description>
<dc:subject>nodejs tutorial inls620</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:6d6131dbb971/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:nodejs"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:tutorial"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:inls620"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://aphyr.com/tags/Clojure-from-the-ground-up">
    <title>Clojure from the ground up</title>
    <dc:date>2014-01-23T23:18:57+00:00</dc:date>
    <link>http://aphyr.com/tags/Clojure-from-the-ground-up</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[This guide aims to introduce newcomers and experienced programmers alike to the beauty of functional programming, starting with the simplest building blocks of software. You’ll need a computer, basic proficiency in the command line, a text editor, and an internet connection. By the end of this series, you’ll have a thorough command of the Clojure programming language.]]></description>
<dc:subject>clojure tutorial</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:aa5950fd513c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:clojure"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:tutorial"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://en.wikibooks.org/wiki/Haskell/Write_Yourself_a_Scheme_in_48_Hours">
    <title>Write Yourself a Scheme in 48 Hours - Wikibooks, open books for an open world</title>
    <dc:date>2014-01-22T14:30:22+00:00</dc:date>
    <link>http://en.wikibooks.org/wiki/Haskell/Write_Yourself_a_Scheme_in_48_Hours</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[This tutorial takes a different tack. You'll start off with command-line arguments and parsing, and progress to writing a fully-functional Scheme interpreter that implements a good-sized subset of R5RS Scheme. Along the way, you'll learn Haskell's I/O, mutable state, dynamic typing, error handling, and parsing features. By the time you finish, you should be fairly fluent in both Haskell and Scheme.
There are two main audiences targeted by this tutorial:
People who already know Lisp or Scheme and want to learn Haskell
People who don't know any programming language, but have a strong quantitative background and are familiar with computers]]></description>
<dc:subject>haskell tutorial</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:12dd1c87b495/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:haskell"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:tutorial"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.haskell.org/haskellwiki/A_brief_introduction_to_Haskell">
    <title>A brief introduction to Haskell - HaskellWiki</title>
    <dc:date>2014-01-22T14:28:43+00:00</dc:date>
    <link>http://www.haskell.org/haskellwiki/A_brief_introduction_to_Haskell</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Haskell is:

A language developed by the programming languages research community.
Is a lazy, purely functional language (that also has imperative features such as side effects and mutable state, along with strict evaluation)
Born as an open source vehicle for programming language research
One of the youngest children of ML and Lisp
Particularly useful for programs that manipulate data structures (such as compilers and interpreters), and for concurrent/parallel programming]]></description>
<dc:subject>haskell tutorial</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:2bf1266e9e99/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:haskell"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:tutorial"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.umiacs.umd.edu/~hal/docs/daume02yaht.pdf">
    <title>Yet Another Haskell Tutorial</title>
    <dc:date>2014-01-22T14:25:48+00:00</dc:date>
    <link>http://www.umiacs.umd.edu/~hal/docs/daume02yaht.pdf</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[The goal of the Yet Another Haskell Tutorial is to provide a complete introduction to the Haskell programming language. It assumes no knowledge of the Haskell language or familiarity with functional programming in general.]]></description>
<dc:subject>haskell tutorial</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:52f89e19c598/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:haskell"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:tutorial"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.braveclojure.com/">
    <title>Clojure for the Brave and True, an Online Book for Beginners</title>
    <dc:date>2014-01-10T15:12:45+00:00</dc:date>
    <link>http://www.braveclojure.com/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[To wield Clojure to its fullest, you will need to find your way through the four labyrinths confronting every programmer learning a new language:

The Cave of Artifacts. In its depths you'll learn to build, run, and distribute your own programs and use the libraries of others. You'll learn Clojure's relationship to the JVM (Java Virtual Machine).
The Forest of Tooling. It's paramount to set up your environment so that you can quickly try things out and learn from them.
The Mountain of Language. As you ascend, you'll gain knowledge of Clojure's syntax, semantics, and data structures.
The Cloud Castle of Mindset. In its rarified air you will come to know the why and how of lisp and functional programming.]]></description>
<dc:subject>clojure book tutorial</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:31bf1a2ee7fe/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:clojure"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:book"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:tutorial"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://bost.ocks.org/mike/selection/">
    <title>How Selections Work</title>
    <dc:date>2013-04-26T19:24:20+00:00</dc:date>
    <link>http://bost.ocks.org/mike/selection/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[In the past I have presented simplified descriptions of D3’s selections, providing only enough detail to get started. This article takes a more comprehensive approach; rather than saying how to use selections, I will explain how selections are implemented. This may take longer to read, but it should dispel any magic and help you master data-driven documents.]]></description>
<dc:subject>d3 tutorial infoviz javascript</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:552438b69984/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:d3"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:tutorial"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:infoviz"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:javascript"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://lifehacker.com/5983680/how-the-heck-do-i-use-github">
    <title>How the Heck Do I Use GitHub?</title>
    <dc:date>2013-02-13T12:07:51+00:00</dc:date>
    <link>http://lifehacker.com/5983680/how-the-heck-do-i-use-github</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[GitHub's a great tool but it's definitely a little confusing the first time around (and, possibly, a few times after that). That's likely why GitHub created software (for OS X and Windows) to make the process a bit easier. Nevertheless, it's good to learn the old-fashioned way otherwise your options in the simplified software won't make sense. Let's start by walking through the basics.]]></description>
<dc:subject>git github tutorial inls620</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:8b54b886a2d4/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:git"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:github"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:tutorial"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:inls620"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.mlsurveys.com/">
    <title>Machine Learning Surveys</title>
    <dc:date>2013-01-05T21:31:27+00:00</dc:date>
    <link>http://www.mlsurveys.com/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[A list of literature surveys, reviews, and tutorials on Machine Learning and related topics.]]></description>
<dc:subject>machinelearning research tutorial</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:a0a2e6f171cf/</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:research"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:tutorial"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.socher.org/index.php/DeepLearningTutorial/DeepLearningTutorial">
    <title>Deep Learning Tutorial - www.socher.org</title>
    <dc:date>2012-12-22T02:37:43+00:00</dc:date>
    <link>http://www.socher.org/index.php/DeepLearningTutorial/DeepLearningTutorial</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Machine learning is everywhere in today's NLP, but by and large machine learning amounts to numerical optimization of weights for human designed representations and features. The goal of deep learning is to explore how computers can take advantage of data to develop features and representations appropriate for complex interpretation tasks. This tutorial aims to cover the basic motivation, ideas, models and learning algorithms in deep learning for natural language processing. Recently, these methods have been shown to perform very well on various NLP tasks such as language modeling, POS tagging, named entity recognition, sentiment analysis and paraphrase detection, among others. The most attractive quality of these techniques is that they can perform well without any external hand-designed resources or time-intensive feature engineering. Despite these advantages, many researchers in NLP are not familiar with these methods. Our focus is on insight and understanding, using graphical illustrations and simple, intuitive derivations. The goal of the tutorial is to make the inner workings of these techniques transparent, intuitive and their results interpretable, rather than black boxes labeled "magic here". The first part of the tutorial presents the basics of neural networks, neural word vectors, several simple models based on local windows and the math and algorithms of training via backpropagation. In this section applications include language modeling and POS tagging. In the second section we present recursive neural networks which can learn structured tree outputs as well as vector representations for phrases and sentences. We cover both equations as well as applications. We show how training can be achieved by a modified version of the backpropagation algorithm introduced before. These modifications allow the algorithm to work on tree structures. Applications include sentiment analysis and paraphrase detection. We also draw connections to recent work in semantic compositionality in vector spaces. The principle goal, again, is to make these methods appear intuitive and interpretable rather than mathematically confusing. By this point in the tutorial, the audience members should have a clear understanding of how to build a deep learning system for word-, sentence- and document-level tasks. The last part of the tutorial gives a general overview of the different applications of deep learning in NLP, including bag of words models. We will provide a discussion of NLP-oriented issues in modeling, interpretation, representational power, and optimization.]]></description>
<dc:subject>machinelearning nlp tutorial deeplearning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:6c28a672bea5/</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:nlp"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:tutorial"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:deeplearning"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://yannesposito.com/Scratch/en/blog/Yesod-tutorial-for-newbies/">
    <title>Haskell web programming</title>
    <dc:date>2012-12-20T22:44:26+00:00</dc:date>
    <link>http://yannesposito.com/Scratch/en/blog/Yesod-tutorial-for-newbies/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[A simple yesod tutorial. Yesod is a Haskell web framework.]]></description>
<dc:subject>haskell yesod web framework tutorial</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:7daee9ff7eb2/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:haskell"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:yesod"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:web"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:framework"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:tutorial"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.cs.utah.edu/~hal/docs/daume02yaht.pdf">
    <title>Yet Another Haskell Tutorial</title>
    <dc:date>2012-12-14T16:58:28+00:00</dc:date>
    <link>http://www.cs.utah.edu/~hal/docs/daume02yaht.pdf</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[The goal of the Yet Another Haskell Tutorial is to provide a complete intoduction to the Haskell programming language. It assumes no knowledge of the Haskell language or familiarity with functional programming in general.]]></description>
<dc:subject>haskell tutorial</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:9da3793ef4a2/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:haskell"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:tutorial"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.haskell.org/tutorial/">
    <title>A Gentle Introduction to Haskell, Version 98</title>
    <dc:date>2012-12-14T16:57:27+00:00</dc:date>
    <link>http://www.haskell.org/tutorial/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Our purpose in writing this tutorial is not to teach programming, nor even to teach functional programming. Rather, it is intended to serve as a supplement to the Haskell Report [4], which is otherwise a rather dense technical exposition. Our goal is to provide a gentle introduction to Haskell for someone who has experience with at least one other language, preferably a functional language (even if only an "almost-functional" language such as ML or Scheme).]]></description>
<dc:subject>haskell tutorial</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:688b0f697be3/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:haskell"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:tutorial"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://java.ociweb.com/mark/clojure/article.html">
    <title>Clojure - Functional Programming for the JVM</title>
    <dc:date>2012-12-14T16:31:38+00:00</dc:date>
    <link>http://java.ociweb.com/mark/clojure/article.html</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[The goal of this article is to provide a fairly comprehensive introduction to the Clojure programming language. A large number of features are covered, each in a fairly brief manner.]]></description>
<dc:subject>clojure tutorial</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:2da8539a389e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:clojure"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:tutorial"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://math-blog.com/2012/09/24/how-to-build-a-recommendation-engine/">
    <title>How to Build a Recommendation Engine</title>
    <dc:date>2012-09-25T01:33:35+00:00</dc:date>
    <link>http://math-blog.com/2012/09/24/how-to-build-a-recommendation-engine/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[This article shows how to build a simple recommendation engine using GNU Octave, a high-level interpreted language, primarily intended for numerical computations, that is mostly compatible with MATLAB. ]]></description>
<dc:subject>matlab recommendation math statistics tutorial</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:429640e1b39a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:matlab"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:recommendation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:math"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:rybesh/t:tutorial"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://docs.datomic.com/tutorial.html">
    <title>Datomic Development Resources</title>
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<item rdf:about="http://vimeo.com/20717301">
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<item rdf:about="http://clojurescriptone.com/">
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    <link>http://clojurescriptone.com/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[ClojureScript One shows you how to use ClojureScript to build single-page, single-language applications]]></description>
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<item rdf:about="http://book.realworldhaskell.org/read/">
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<item rdf:about="http://golang.org/doc/effective_go.html">
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<item rdf:about="http://try.github.com/">
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    <dc:date>2012-07-04T21:06:02+00:00</dc:date>
    <link>http://try.github.com/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[A unique and easy way, in the format of a Code School interactive course, for new Git and GitHub users to try both the tool and the service without a single bit of software installation.]]></description>
<dc:subject>git tutorial inls620</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
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<item rdf:about="http://nlp.stanford.edu/~manning/courses/DigitalHumanities/">
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    <dc:creator>rybesh</dc:creator><description><![CDATA[Chris Manning's tutorial at Digital Humanities 2011 at Stanford.]]></description>
<dc:subject>nlp tutorial digitalhumanities</dc:subject>
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<item rdf:about="http://nodeguide.com/beginner.html">
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    <link>http://nodeguide.com/beginner.html</link>
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<item rdf:about="http://autotelicum.github.com/Smooth-CoffeeScript/interactive/interactive-coffeescript.html">
    <title>Interactive Smooth CoffeeScript</title>
    <dc:date>2012-02-24T02:56:26+00:00</dc:date>
    <link>http://autotelicum.github.com/Smooth-CoffeeScript/interactive/interactive-coffeescript.html</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Discover the beauty of functional programming in Coffeescript. The source of this book is a literate markdown program/document which produces this code: CoffeeScript, that translates into JavaScript and produces this output.]]></description>
<dc:subject>coffeescript programming tutorial publishing authoring markdown functional</dc:subject>
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<item rdf:about="http://www.codecademy.com/#!/exercises/0">
    <title>Learn to code | Codecademy</title>
    <dc:date>2012-01-10T15:45:12+00:00</dc:date>
    <link>http://www.codecademy.com/#!/exercises/0</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Codecademy is the easiest way to learn how to code.]]></description>
<dc:subject>programming tutorial education inls620</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
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<item rdf:about="http://peepcode.com/products/nodejs-i">
    <title>Node.js Tutorial | PeepCode Screencast</title>
    <dc:date>2012-01-02T18:04:43+00:00</dc:date>
    <link>http://peepcode.com/products/nodejs-i</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[In this 70-minute Node.js tutorial, you’ll learn to install, use, and understand Node by building a real-time geographical tracking system (live demo). We start with simple servers, static requests, and dynamically-generated content and then we explore persistent connections and client-side scripting.]]></description>
<dc:subject>nodejs tutorial</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:190e4d710b0a/</dc:identifier>
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<item rdf:about="http://nodebeginner.org/">
    <title>The Node Beginner Book » A comprehensive Node.js tutorial</title>
    <dc:date>2012-01-02T18:04:30+00:00</dc:date>
    <link>http://nodebeginner.org/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[The aim of this document is to get you started with developing applications with Node.js, teaching you everything you need to know about "advanced" JavaScript along the way. It goes way beyond your typical "Hello World" tutorial.]]></description>
<dc:subject>nodejs tutorial</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:rybesh/b:013ee2f6bc85/</dc:identifier>
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<item rdf:about="http://eloquentjavascript.net/">
    <title>Eloquent JavaScript: A Modern Introduction to Programming</title>
    <dc:date>2012-01-02T18:01:16+00:00</dc:date>
    <link>http://eloquentjavascript.net/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Eloquent JavaScript is a book providing an introduction to the JavaScript programming language and programming in general.]]></description>
<dc:subject>javascript programming tutorial</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
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<item rdf:about="http://simonwillison.net/static/2010/redis-tutorial/">
    <title>Redis tutorial, April 2010 - by Simon Willison</title>
    <dc:date>2011-11-08T22:37:17+00:00</dc:date>
    <link>http://simonwillison.net/static/2010/redis-tutorial/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[These slides and notes were originally written to accompany a three hour Redis tutorial I gave at the NoSQL Europe conference on the 22nd of April 2010.]]></description>
<dc:subject>redis howto nosql tutorial</dc:subject>
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<item rdf:about="http://docs.nodejitsu.com/">
    <title>node docs</title>
    <dc:date>2011-09-21T02:20:58+00:00</dc:date>
    <link>http://docs.nodejitsu.com/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[We believe in sharing knowledge. So we have assembled this growing collection of node.js how-to articles. These articles range from basic to advanced. They provide relevant code samples and insights into the design and philosophy of node itself.

docs.nodejitsu.com is an open source project and is curated by the Nodejitsu team and friends. If you have articles or ideas that you would like to contribute, we'd very much like to accept your pull request!]]></description>
<dc:subject>nodejs tutorial documentation</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
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<item rdf:about="http://projects.serenity.de/textmate/">
    <title>Project TextMate</title>
    <dc:date>2011-09-14T02:51:56+00:00</dc:date>
    <link>http://projects.serenity.de/textmate/</link>
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The Basics Tutorial shows off the great features of TextMate and gives you a good start at understanding the concept & the guts of the Editor as well as a good idea of how you can enhance it and contribute yourself.]]></description>
<dc:subject>textmate tutorial howto editing</dc:subject>
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<item rdf:about="http://www.mihswat.com/2011/05/04/getting-started-with-cloud-foundry-using-a-node-js-and-mongodb-application/">
    <title>Getting started with Cloud Foundry using a Node.js and MongoDB application | MIH SWAT</title>
    <dc:date>2011-08-31T11:36:18+00:00</dc:date>
    <link>http://www.mihswat.com/2011/05/04/getting-started-with-cloud-foundry-using-a-node-js-and-mongodb-application/</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[Example CF app using node and mongodb.]]></description>
<dc:subject>cloudfoundry mongodb nodejs tutorial</dc:subject>
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<item rdf:about="http://groovy.codehaus.org/Beginners+Tutorial">
    <title>Groovy - Beginners Tutorial</title>
    <dc:date>2011-06-15T02:37:14+00:00</dc:date>
    <link>http://groovy.codehaus.org/Beginners+Tutorial</link>
    <dc:creator>rybesh</dc:creator><description><![CDATA[This page is intended to get you started with Groovy, following a trail of a few tutorial labs on various topics mainly oriented towards typical use of scripting languages for data crunching or text manipulation.]]></description>
<dc:subject>groovy scripting java tutorial howto</dc:subject>
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