The implementations look odd. A network consists of a collection of neurons, which are implemented individually as structs. The forward pass through the network

The implementations look odd. A network consists of a collection of neurons, which are implemented individually as structs. The forward pass through the network is a series of nested loops, and the gradient descent implementation doesn't use backpropagation - it uses finite differences to approximate derivatives, which is known to be inefficient. Given the overall design of the library, it isn't really clear what you would use it for in practice. I hope that future versions take inspiration from other open source machine learning libraries, which show how to use linear algebra and backpropagation and are much more effective.