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PyTorch comes with a rich set of built-in optimizers like SGD, Adam, and RMSProp — more than enough for most deep learning tasks. But what if you’re reading a cutting-edge paper with a brand-new optimizer? Or maybe you’ve come up with your own twist on an existing algorithm and want to see how it performs. That’s where writing your own optimizer comes in. In this hands-on tutorial, we’ll demystify the process by walking through how to create a custom optimizer in PyTorch.
What is the biggest unsolved problem? If you Google this question, you might find a Wikipedia page titled ‘List of unsolved problems.’ There, you will find many sublists, each containing a number of open problems within its respective field. You might also ask ChatGPT, read other blogs detailing some problems, or turn to the 7 open problems (now 6) posed by the Clay Mathematics Institute, where the prize for solving any one of them is a million dollars each.
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