hopfield-nets
Hopfield Nets were one of the first success stories of the Connectionists, and they operate on a somewhat different principle than the DNNs that are so popular today. In essense, Hopfield networks are a specific class of Connectionist energy based neural networks, whereby relationships between neurons/pixels are impressed into the weights as minimum energy states.
This allows us to store patterns and later recover them from their corrupted versions - the so called, "content addressable networks", which are somewhat akin to how we are so easily able to recall memories by only being given certain cues.
The above animation shows a hopfield network at work, completely recovering a corrupted pattern.
This repo will walk you through the process of building a Hopfield network. To do so, simply run hopfield.ipynb via:
ipython notebook hopfield.ipynb