|Written by Mike James|
|Thursday, 23 October 2014|
Synaptic implements a general "architecture free" algorithm that can be used to create a wider range of network types than usually encountered. It comes with some predefined networks - multilayer perceptrons, multilayer long-short term memory networks, liquid state machines, and so on. The key difference is that Synaptic allows you to create second order and recurrent networks, which are not as commonly encountered as simple feed forward networks and are traditionally regarded as harder to work with.
There are also some nice examples, but be warned, if you try them out you might see an error page due to the author's account being over its CPU limit. This is a low budget production that could benefit from some sponsorship.
Once you get beyond the demos then you will have to learn how to create layers of neurons. To train the model all you have to do is provide an input and a target output. You can work with layers or complete networks of layers and even models using multiple networks and more.
It is difficult to give an idea of how sophisticated this all is and the best way to appreciate it is to take a look at the GitHub page.
This is a project that is worth supporting as well as using. As the author says at the end of the Read.me:
"Anybody in the world is welcome to contribute in the development of the project."
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|Last Updated ( Thursday, 23 October 2014 )|