kann - A lightweight C library for artificial neural networks
KANN is a standalone and lightweight library in C for constructing and training small to medium artificial neural networks such as multi-layer perceptrons, convolutional neural networks and recurrent neural networks (including LSTM and GRU). It implements graph-based reverse-mode automatic differentiation and allows to build topologically complex neural networks with recurrence, shared weights and multiple inputs/outputs/costs. In comparison to mainstream deep learning frameworks such as TensorFlow, KANN is not as scalable, but it is close in flexibility, has a much smaller code base and only depends on the standard C library. In comparison to other lightweight frameworks such as tiny-dnn, KANN is still smaller, times faster and much more versatile, supporting RNN, VAE and non-standard neural networks that may fail these lightweight frameworks. KANN could be potentially useful when you want to experiment small to medium neural networks in C/C++, to deploy no-so-large models without worrying about dependency hell, or to learn the internals of deep learning libraries.