<story><title>How to Get a Job in Deep Learning</title><url>http://blog.deepgram.com/how-to-get-a-job-in-deep-learning/</url></story><parent_chain></parent_chain><comment><author>csantini</author><text>TL;DR: Deep Learning will become a commodity. Software will eat Deep Learning too.&lt;p&gt;I&amp;#x27;d like to clean up a bit the air from the hype fog:&lt;p&gt;DL is giving amazing results only when you have big sets of labelled data. Hence it will be much cheaper for companies to buy Google&amp;#x2F;Microsoft Vision&amp;#x2F;Audio REST APIs rather than paying the costs of: cloud + find data + deep learning experts. So, I don&amp;#x27;t think we will see a massive growth of DL gigs.&lt;p&gt;e.g. Google Vision API: &lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;cloud.google.com&amp;#x2F;vision&amp;#x2F;&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;cloud.google.com&amp;#x2F;vision&amp;#x2F;&lt;/a&gt;&lt;p&gt;Except those areas where your own CNN implementation is needed (automotive, industrial automation), Deep Learning will be another &amp;quot;library&amp;quot; in the ever increasing Software Engineering mess of gluing many open source libraries and REST apis to get something useful done. You need 1 guy training a Neural Network for every 100 software monkeys maintaining the infrastructure complexity.
There are now many Software Engineering jobs because it&amp;#x27;s hard to glue and maintain publicly-available code to solve some specific business problem.&lt;p&gt;I think the the same applies for many Data Scientist jobs, which are these days more about fetching&amp;#x2F;cleaning&amp;#x2F;visualizing data than making machine learning on it.</text></comment>
<story><title>How to Get a Job in Deep Learning</title><url>http://blog.deepgram.com/how-to-get-a-job-in-deep-learning/</url></story><parent_chain></parent_chain><comment><author>orthoganol</author><text>My question is, it feels like machine learning is reaching its &amp;quot;Rails&amp;quot; stage. You can implement the latest Bi-directional NN or LSTM-RNN using a high level API that already sits on top of another high level framework. Even beyond the core setup it will do the peripherals - smart initializations, anti-overfitting, split up your data, etc.&lt;p&gt;Do people who implement (albeit real, useful) deep learning systems, but who have no formal machine learning background, who don&amp;#x27;t really know much or care about implementing derivatives or softmax functions because the frameworks abstract all that away - are these people getting offered jobs?</text></comment>