Implementing KANs From Scratch Using PyTorch

Last week, we understood the underlying technical details of Kolmogorov Arnold Networks (KANs) and how they work.

Today, we are continuing that series and learning how to implement them from scratch using PyTorch.

Read it here: Implementing KANs From Scratch Using PyTorch.

Yet again, we are doing it in an entirely beginner-friendly way.

But why are we learning to implement them?

I find this important because we all know how to build and train a neural network with regular weight matrices.

However, KANs are based on a different idea.

The matrices KANs possess in a layer do not contain weights but functions, which are applied to the input of that layer.

Thus, by implementing KANs, we can learn how a network that does not contain the traditional weight matrices but rather univariate functions formed using B-splines can be trained.

Read it here: Implementing KANs From Scratch Using PyTorch.

Have a good day!

Avi

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