Do neural networks create a separating hyperplane? https://colah.github.io/posts/2014-03-NN-Manifolds-Topology/ mentions that neural networks learn a representation of the data so as to make the classes linearly separable. What I fail to see is how does a neural network create a separating hyperplane that separates the classes. How do we know that it creates a hyperplane? What's the math behind it?

The final layer can be seen as a generalized linear model (such a a logistic regression) on extracted features.
Consider a basic network with an input layer, a hidden layer, and an output layer. Draw it out and then cover up the input layer. Now it looks like a linear or generalized linear model (such as logistic regression) on some features.
That’s what’s happening and what the author means: the network uses early layers to figure out the features. Then the network regresses on those features.
