Side Quest: parameter estimate for differential equations

Hi all,

I got a little side quest from a friend doing PhD in NTU. To put it simply, there is a cubic differential equation whose parameters are to be fit with existing data. Since I was playing around with gradient descent recently, I decide to give it a try. You can view it in this link Parameter Estimation for Differential Equations using Gradient Descent including a little more technical note. For that I also write a very simple Runge-Kutta wrapper in this link.

Update: I tested kero 0.6.3 Neural Network library on MNIST here; more work needs to be done!

‘Til next time!

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