REVISED August 13th 2017
Almost all mature deep neural network (DNN) libraries e.g. Tensor Flow, Theano, Caffe, and etc are written in python, not in C/C++. We will hardly find library for DNN written in C/C++. Even if we find one, it requires heavy resources. Fortunately, we now have tiny-dnn. tiny-dnn is a C++11 implementation of deep learning. Nothing needs to be compiled, header only. It is suitable for deep learning on limited computational resource, embedded systems and IoT devices.
For new tiny-dnn user, it may hard to get used with the environment because the examples provided are directly designated to solve MNIST or CIFAR problem. On this post, I try to give example to solve simple problem (XOR) using tiny-dnn. It may sound excessive to use DNN framework only to solve XOR problem,. But for the sake of better understanding of framework structure, I think it’s okay to do so.
