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).
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).