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Last Commit
Oct. 22, 2018
Aug. 19, 2018


This is a small pure C header-only, zero-dependency library for arbitrarily deep neural networks. It is my first attempt to write a scientific project in C but the speed is already outstanding. I made this library in order to assist me with my doctoral research.

It is CPU only (soon with support for cuBLAS and cuDNN if I get my hands on an NVIDIA GPU). I have plans to extend it for CNNs.


The procedure does not have a hardcoded depth (it can have as many layers with as many nodes as you want-beware of your heap limits).

Getting Started

The library is created with Linux machines in mind but OSX users should not have a problem if they have gcc installed. I will try to compile an example with Visual Studio and get back to you on how to do it.

In order to get libartificial you have to do the following (assuming working installation of git)

git clone
cd libartificial
rm -rf .git


For the time being I have four examples which you can find in the "examples" folder (assuming make and gcc):

  • MLP regression (rbf like: gaussian & linear w/ RMSE):
make test1
  • MLP classification (logistic & softmax w/ cross-entropy):
make test2
  • Autoencoder:
make test3
  • CNN (only im2col at the moment):
make test4

If you want to compile them all then just do



If you like my work and/or you want to use it for your own projects or want me to create a custom recipe for you, I would gladly accept your donations at:


ETH: 0xf09fce52f7ecd940cae2826deae151b6495354f6


Copyright (c) Jim Karoukis. This project is licensed under the MIT License - see the LICENSE file for details.