<hr/>**评论：**<br/><br/>chewxy: <pre><p>Yes. I did most of my commercial deep learning work in Go. The ability to send your clients a single executable that compiles in seconds is most useful.</p>
<p>The libraries I use are <a href="https://github.com/gorgonia/gorgonia" rel="nofollow">Gorgonia</a> and <a href="https://github.com/gonum" rel="nofollow">Gonum</a>. </p></pre>recurrency: <pre><ul>
<li><code>gonum</code></li>
</ul></pre>circuitously: <pre><p>Also, Is there any kind of gpu support out there yet?</p></pre>packetlust: <pre><p>Disclaimer: I do not know from first hand use</p>
<p>I was told by a data science person at a recent Go meetup that Gorgonia did/does do GPU acceleration. He also said that it was a little broken at the moment, but he expected it would be fixed soon</p></pre>chewxy: <pre><p>I broke it, fixed it in 0.6, broke it in 0.7, fixed in 0.8. In the upcoming version (0.9) is all about improving CUDA. I've a lot of personal code to backport into gorgonia</p></pre>chewxy: <pre><p><a href="https://gorgonia.org/cu" rel="nofollow">https://gorgonia.org/cu</a>. Pull Requests extremely welcome. Works with CUDA 9.0</p></pre>circuitously: <pre><p>Thanks!</p></pre>ka_o: <pre><p>Some libs: blas, GoLearn, Gorgonia, TensorFlow</p></pre>weberc2: <pre><p>Pinging <a href="/u/sbinet" rel="nofollow">/u/sbinet</a></p></pre>sbinet: <pre><p>I'd say you can do good DL/ML stuff in Go.</p>
<p>Not everything is there on the shelf, ready to be imported.
the "half empty glass" way to look at it is that it's a good time to contribute the building blocks (or just the nice user-facing API) for your favorite problem.</p>
<p>here is an old-ish introductory lecture I gave:</p>
<ul>
<li><a href="https://github.com/sbinet/jdev-go-datascience-2017" rel="nofollow">https://github.com/sbinet/jdev-go-datascience-2017</a></li>
</ul></pre>_nefario_: <pre><p>not quite yet</p>
<p><a href="https://www.youtube.com/watch?v=lcyNjgEG9H8" rel="nofollow">https://www.youtube.com/watch?v=lcyNjgEG9H8</a></p></pre>
