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!