[deepnet]

Neural Turing Machines by Alex Graves, Greg Wayne, Ivo Danihelka

https://arxiv.org/abs/1410.5401

A Neural Turing machine is a type of neural network with addressable memory - it learns rather than being explicity programmed.

The net also learns to boot, i.e. how to read/ write it's own memory from scratch.

Stochastic Gradient Descent learning relies on backpropagation of the error corrections to the individual weights to make a slight improvement - this requires a differentiable neural net.

This version innovates with content addressable memory. The authors demonstrate learning from big data sets and with reinforcement learning, (i.e. trial and error as an embodied agent).