NewsPoem – Wednesday, June 3rd, 2020
Deep Learning, Digital Twins, Digital Twins Data Gap, Digital Twins, DL applications,
News First, Poem Follows –
Excerpt from www.eetasia.com
Digital Twins Bridge the Data Gap for Deep Learning
As companies start to explore DL and how it can help them, many are finding two things: first, it’s easy to get to a DL prototype, and second, it’s harder to get from “good prototype” results to “production-quality” results. With all of the low- to no-cost DL platforms, tools, and kits available today, initial development for DL applications is very quick and relatively easy in comparison to conventional application development. However, productizing DL applications isn’t any easier — and can be harder — than productizing conventional applications. The reason for this is data. Having enough data — and enough of the right kinds of data — is very often the difference between a DL application that doesn’t deliver production-quality results and one that revolutionizes the way you approach a particular problem…..
The success of any DL application depends on the depth and breadth of the data set used in training. If the training data set is too small, too narrow, or too “normal,” a DL approach will not do better than standard techniques — in fact, it might do worse. It’s important to train a network with data representing all important states or presentations, in sufficient volumes for the network to learn to capture the correct essence of the problem at hand.
The difficulty for some fields, such as autonomous driving or semiconductor manufacturing, is that some of the most serious anomalous conditions occur (thankfully) very rarely. However, if you want a DL application to recognize a child darting in front of a car — or a fatal photomask error — you have to train the networks with a multitude of these scenarios, which don’t exist in any great volume in the real world. Digital twins are the only way to create enough anomalous data to properly train the networks to recognize these conditions.
Ideally, to maintain full control over the data, you need three digital twins: a digital twin of the process/equipment that precedes yours in the manufacturing flow to provide input data for the simulation of your own process; a digital twin of your own process/equipment; and a digital twin of the process/equipment that follows yours in the manufacturing flow so that you can feed your output downstream for validation.
Source News Link - https://freeqo.com/2020/06/03/deep-learners-blast-the-uncommon-out-of-the-sense/
News Curated by Obamacare.TV
Deep Learners Blast the Uncommon Out of the Sense
Deep.
TwinnedDeep learning. the capture. I blamed my fallow fields for the drought of my mind but
my viscera
tramples the blades of glades with terrible spinning. Ideally,to maintain the data. Twins.
if you want to spell, or cast them, or….
downstream, validation feeds your light over your bed…..
the dim fortitude briefly spatters words into your retina, and you assume
the difficulty with some forms, fields,
Digital Twins metric the diametrical flow, the dystopian flaws in the assaults,
the literal translation of the act of the blink, the rolling eye’s datapoints
spiraled into the potential range of values.
productizing. Twins.
the illiterate logic we have yet to maintain, learning, deep, deep
penetrating the augury’s mystical reading of sinews,
blood, the bleached, diseased kidneys,
the gesture…
It wasn’t the need for an answer, it was the need for the end of the campaign.
We do this. yes.
Good. Freedom from choice. Processing.
Deep, deep
network a train, a ticking time bomb blasting the uncommon out of the machine’s sense.
