Talking about Technology

.NET Foundation

Making software is hard. Every once in a while we find some code that really helps the process. It makes our own code intelligible: almost fun! Sometimes the magical code is of our own making but often we are using someone else’s code. In either case the next step should be to share the joy.

My preferred mechanism to share the joy is through videos. Videos are a powerful way of humanizing the types of problems software solves. With video, you can get in the thick of it code-wise while still adding a literal face. You can see the excitement, the technology, and most importantly: the code.

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A Brief Forage into Functional Thinking

I have long considered software engineering to be a craft that must be honed over years of careful study and precise implementation. What we learn in the labs of school and contrived examples are indeed important but only when carefully used in our day to day work. As such my challenge to you, dear reader, is to try to find at least one application where thinking functionally might assist you in your daily endeavors to produce excellent software.
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Descriptors in numl

As some of you know I have been working on a machine learning library for .NET called numl. The main purpose of the library is to abstract away some of the mundane issues surrounding setting up the learning problem in the first place. Additionally sometimes the math in machine learning seems to be a bit daunting (some of it is indeed daunting) so the library allows you to either get into the math or trust that these things are implemented and run correctly.

In order to facilitate this type of abstraction I came to realize that the best way to bridge this gap was to use constructions that most would have already either used or understood: classes. The learning problem, as I understood it, was taking a set of things and trying to learn a way to predict a particular aspect of these things. The best approach therefore was to allow for an easy way to markup these things (or classes) in order to produce an efficient technique for setting up the learning problem.

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Codemash 2.0.1.3

It was absolutely a blast to be able to present my new machine learning library at CodeMash this year. One of the key goals of the library is to ensure that it is readily accessible to all of its users. Machine learning can often be an intimidating subject with its esoteric terms and complex math. This library is designed to ease the process of feature selection (more on that later) and training. This is obviously a work in progress and any input is welcome (and wanted). If you’d like to get started head on over to the site to learn how to get started using nuML.
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Supervised Learning - Classification

Supervised Learning

In supervised learning, the algorithm is given labeled examples in order to come up with an appropriate model that defines the data and can also correctly label future examples correctly (or adequately). Supervised learning can be grouped into the following depending on the actual label type:

  1. Binary Classification (think yes/no)
  2. Multi-class classification (any answer from a finite set)
  3. Rgression (any answer from an infinite set)

In the machine library I am trying to put together, each of the three groups mentioned above can be separated into distinct .NET data types as follows:

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