
Microsoft Introduced NeuronBlocks – Modulized NLP DNN Toolkit - lisho
https://github.com/microsoft/NeuronBlocks
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keithnoizu
Sounds vaguely like my caffeine fueled musing:
[https://github.com/noizu/artificial_intelligence](https://github.com/noizu/artificial_intelligence)
"3\. Working rudimentary memory, and reusable modular networks.

So keeping in mind of the concepts from 1. & 2\. and a large amount of
computing power plus some caveats it should be possible to train modular deep
networks, that can be dropped into existing solutions and capable of altering
behavior/focus based on upstream signals, with minimal additional training,
and without the need to alter the training on the core module. The training
process however becomes much more involved.

The approach I would propose here is to,

Stage 1

Prepare a deep network in the usual manner. Define output sections. (These
nodes must contain the make of the car in the image I am viewing) (those nodes
must encode the color of the car, etc.). a. Break the output layer into
regions that must contain specific data in it’s entirety. b. Train neural
networks against those regions to answer or show specific data. Use these
split off networks to reinforce learning. c. Once the network is sufficiently
trained such that each of the split off networks correctly interprets the data
to a sufficient degree re run the network and record the output of all of the
split off networks and the intermediate layer they split from d. Repeat,
breaking up large sub layers into smaller sections that can organize machine
data into specific segments that can be routed to other components.

"

