<story><title>Ersatz - Deep neural networks in the cloud</title><url>http://www.ersatz1.com</url></story><parent_chain></parent_chain><comment><author>bravura</author><text>Recommendation: Reach out to my colleague James Bergstra, and build out automatic hyperparameter selection. This will make your offering work off-the-shelf, which is what is necessary for it to see wider adoption.&lt;p&gt;Why? The real pain in the ass in training a deep network is the hyperparameter selection.&lt;p&gt;What is your learning rate? What is your noise level? What is your regularization parameter?&lt;p&gt;Choosing these values is a far bigger pain than almost everything else combined.&lt;p&gt;Doing a grid search is intractable. Random hyperparameter search is better. You can use a sophisticated strategy, like Bergstra et al have proposed.</text></comment>
<story><title>Ersatz - Deep neural networks in the cloud</title><url>http://www.ersatz1.com</url></story><parent_chain></parent_chain><comment><author>RyanZAG</author><text>Little bit confusing on what this actually is. Is this&lt;p&gt;1) Cloud GPU computation where you upload some special model code that is run on the neural network? ie. your own code&lt;p&gt;2) Upload data and run some pre-specified models on it, such as in the example you have a &apos;-d model=spanish_speech_recognizer&apos; - in which case the offering is all about how many and how good your pre-defined models are.&lt;p&gt;The two different use cases are for completely different target audiences.</text></comment>