Experimental Artificial Neural Network based on non-derivative learning algorithm
- Neurons will only fire when > treshold value (boolean output, binary neurons)
- self made learnig algorithm based on the Hebbian Theory
- create, save and open neural networks in terminal
- train with webcam (works at least in recognizing trained data) or MNIST data (does not seem to work)
- the module can be used as a standalone (terminal) application or as a library with functions to be used in other applications
learning:
- to learn more about matrix multiplication and for a matrix calculator look at https://www.mathsisfun.com
- the free 40 page book from Andy Thomas could be useful: https://adventuresinmachinelearning.com/coding-deep-learning-revolution-sale/
- on backpropagation (derivative): https://www.youtube.com/watch?v=Ilg3gGewQ5U&t=163s
- could be of interest, on alternative learning algorithms: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5467749/
Make numpy faster: http://roman-kh.github.io/numpy-multicore/