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I don& #39;t consider myself a deep learning expert by any means. There are still a lot more things I don& #39;t know than things I know (it& #39;s not even close). I& #39;ve only been working with neural networks since 2009, which is a lot less than many of you.
Besides, I& #39;m not sure that "deep learning experts" exist. People with the highest h-index can& #39;t write a GPU kernel or design a DL ASIC. Nor could they win a Kaggle competition. Nor, for the most part, write reusable code (which is really the core of DL).
Not only that, but when I chat with experts, I& #39;m often surprised by how few of them seem to have a clear mental model of what DL is and how it works. In fact, many big-name researchers often say things that are manifestly untrue and easy to disprove!
Consider that, not long ago, most AI experts knew for a fact that neural networks were a failed avenue. Consider that, in 2013, most of the top names in computer vision were saying that the nascent success of DL might be just a fluke. And remember the debates about local minima?
In general, I& #39;m also not a fan of the idea of an "expert". It makes it sound like there& #39;s some threshold of knowledge beyond which you know it all, you& #39;ve made it (perhaps the threshold is when you reach full professorship).
I don& #39;t think that& #39;s how it works.
I don& #39;t think that& #39;s how it works.
