But maybe something that deepens our understanding on deep neural networks? And transports us to an uber-cool science fantasy? Well, maybe not the later.
I have a background in physics, and I’ve been pursuing problems in machine learning for quite some time now. So my brain often tries to make connections (eh? eh? neural network puns anyone?) between the glorious physics literature, and what seems to be engineers (including myself) struggling to wade through a math dump and explain why deep networks work, theoretically.
My first step towards this was issuing a book from my campus library on Nonlinear Dynamics and Chaos by Strogatz, something I’ve been meaning to read for the longest time. And the next step (though this should have been the first one), was to see if there were other people who had been making these connections before me. And there were! So here are some interesting articles that I came across:
- How to Explain Deep Learning using Chaos and Complexity
- Understanding the depth in deep learning through the lense of chaos theory
Now, I don’t know if I can do as good a job as these guys in simplifying the text, but I’ll surely be posting something on this shortly. Till then, do check these articles out!