When you hear backpropagation, you probably think of machine learning, neural networks, and intimidating math. But even if the concept is new to you there’s no reason to worry. Because if we look closely, backpropagation isn’t just a computer science algorithm for machine learning.
No, backpropagation acts on the philosophy of learning through feedback, and thereby has a lot in common with design thinking.

In this article, I compare design thinking to machine learning to make complex concepts from computer science more graspable. I translate the logic of backprop (backpropagation) into design thinking language, and I illustrate how both follow the same idea: iterative improvement through feedback loops. In the latter half of the article I explain more machine learning concepts, the “bias”, “cost function”, what is “overfittig” and “underfitting”, as well as “activation functions”. And what seems incredibly complicated or simply unknown to you now will be a little bit more clear and relatable by the end of this article. — Read More