TensorFlow howto: a universal approximator inside a neural net

Hello everyone

Here is my new article on universal approximation theorem

TLDR: - I implement a first universal approximator with TesnorFlow and i train it on a sinus function (I show that it actually works) - I use it inside a bigger neural networks to classify the MNIST dataset - I display the learnt activation functions - I show that whatever the learnt activation function is, i get consistently the accuracy 0.98 on the test set - bonus: all the code is open-source

Feel free to ask questions or give feedback or both!