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<!DOCTYPE html>
<html>
<head>
<title>Gorgonia - Deep Learning Library for Go</title>
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<title>About :: Gorgonia - Deep Learning Library for Go</title>
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<meta name="description" content="Gorgonia is a suite of libraries for creating performant deep learning and machine learning applications in Go">
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<a class="baselink" href="https://gorgonia.org/">Gorgonia - Deep Learning Library for Go</a>
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Graph
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<p><img src="https://raw.githubusercontent.com/gorgonia/gorgonia/master/media/mascot_small.jpg" align="right" /></p>
<p>Gorgonia is a library that helps facilitate machine learning in Go. Write and evaluate mathematical equations involving multidimensional arrays easily. If this sounds like <a href="http://deeplearning.net/software/theano/">Theano</a> or <a href="https://www.tensorflow.org/">TensorFlow</a>, it’s because the idea is quite similar. Specifically, the library is pretty low-level, like Theano, but has higher goals like Tensorflow.</p>
<p>Gorgonia:</p>
<ul>
<li>Can perform automatic differentiation</li>
<li>Can perform symbolic differentiation</li>
<li>Can perform gradient descent optimizations</li>
<li>Can perform numerical stabilization</li>
<li>Provides a number of convenience functions to help create neural networks</li>
<li>Is fairly quick (comparable to Theano and Tensorflow’s speed)</li>
<li>Supports CUDA/GPGPU computation (OpenCL not yet supported, send a pull request)</li>
<li>Will support distributed computing</li>
</ul>
<h1 id="goals">Goals</h1>
<p>The primary goal for Gorgonia is to be a <em>highly performant</em> machine learning/graph computation-based library that can scale across multiple machines. It should bring the appeal of Go (simple compilation and deployment process) to the ML world. It’s a long way from there currently, however, the baby steps are already there.</p>
<p>The secondary goal for Gorgonia is to provide a platform for exploration for non-standard deep-learning and neural network related things. This includes things like neo-hebbian learning, corner-cutting algorithms, evolutionary algorithms and the like.</p>
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<i class='fa fa-calendar'></i> Last update on 23/04/2016
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