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<p>GopherData brings together “gophers” (or Go programmers) who use and develop data analysis tools. The organization does this by providing online resources and in-person events. The goals are to provide data analysis enthusiasts, across various domains, with outlets to discuss how best to apply Go and tools built with Go to analytics, scientific research, database development, machine learning, visualization, etc.</p>
<p>We aim to be community-driven and accessible to novice Go programmers, experienced Gophers, data scientists without Go experience, data engineers, and anyone else who is interested in Go for data analysis. On this site you will find related blog posts, a listing of events, and links to a variety of online forums for discussion and learning.</p>
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Wed, Jan 1, 2042,
1st Annual GopherData Conference
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<h3 class="article-title" itemprop="name">
<a href="http://gopherdata.io/post/more_go_based_workflow_tools_in_bioinformatics/" itemprop="url">More Go-based Workflow Tools in Bioinformatics</a>
</h3>
<div class="article-metadata">
<span class="article-date">
<time datetime="2017-11-02 23:10:02 +0100 +0100" itemprop="datePublished">
Thu, Nov 2, 2017
</time>
</span>
<span class="article-tags">
<i class="fa fa-tags"></i>
<a href="/tags/workflows">workflows</a
>,
<a href="/tags/bioinformatics">bioinformatics</a
>
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It is an exciting time for Go as a data science language and for the #gopherdata movement. The ecosystem of tools is constantly improving, with both general purpose tools (e.g., for data frames and statistical analyses) and more specialized ones (e.g., for neural networks and graph-based algorithms) popping up every day.
Recent weeks have been particularly exciting for those involved in the bioinformatics field. In addition to generic libraries for bioinformatics such as bíogo, which was recently reviewed in quite some detail in a two blog posts (part I and part II), the ecosystem of scientific workflow tools focusing on or being used in bioinformatics is also growing: Last week, another Go-based workflow orchestration tool, Reflow, was released as open source, by life science startup Grail Inc.
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<a href="http://gopherdata.io/post/more_go_based_workflow_tools_in_bioinformatics/" class="btn btn-primary btn-outline">
CONTINUE READING
</a>
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<h3 class="article-title" itemprop="name">
<a href="http://gopherdata.io/post/build_ml_powered_game_ai_tensorflow/" itemprop="url">Building an ML-Powered Game AI using TensorFlow in Go</a>
</h3>
<div class="article-metadata">
<span class="article-date">
<time datetime="2017-08-10 08:43:45 +1000 +1000" itemprop="datePublished">
Thu, Aug 10, 2017
</time>
</span>
</div>
<div class="article-style" itemprop="articleBody">
Based on a lightning talk given at GopherCon 2017 “Building an ML-Powered Game AI using TensorFlow in Go” Video / Slides
(Author: Pete Garcin, Developer Advocate @ ActiveState, @rawktron on Twitter and @peteg on Gophers Slack)
For GopherCon, we wanted to demonstrate some of the capabilities of the emerging machine learning and data science ecosystem in Go. Originally built as a demo for PyCon, I had put together a simple arcade space shooter game that features enemies powered by machine learning.
</div>
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<a href="http://gopherdata.io/post/build_ml_powered_game_ai_tensorflow/" class="btn btn-primary btn-outline">
CONTINUE READING
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<div class="article-list-item" itemscope itemprop="blogPost">
<a href="http://gopherdata.io/post/distributed_trump_finder/">
<img src="/img/headers/trump_finder.jpg" class="article-banner" itemprop="image">
</a>
<h3 class="article-title" itemprop="name">
<a href="http://gopherdata.io/post/distributed_trump_finder/" itemprop="url">Building a distributed Trump finder</a>
</h3>
<div class="article-metadata">
<span class="article-date">
<time datetime="2017-04-26 12:00:00 +0000 UTC" itemprop="datePublished">
Wed, Apr 26, 2017
</time>
</span>
<span class="article-tags">
<i class="fa fa-tags"></i>
<a href="/tags/academic">academic</a
>,
<a href="/tags/hugo">hugo</a
>
</span>
</div>
<div class="article-style" itemprop="articleBody">
<p>A step-by-step guide to building a distributed facial recognition system with Pachyderm and Machine Box.</p>
</div>
<p class="read-more">
<a href="http://gopherdata.io/post/distributed_trump_finder/" class="btn btn-primary btn-outline">
CONTINUE READING
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