Neural Networks for Machine Learning
That computer is watching me…
When left to its own devices, what does one of the largest neural networks for machine learning in the world use its 16, 000 computer processors and one billion connections do? Solve complex environmental problems? Crunch scientific data to illuminate the mysteries of deep space? No and no. Turns out, the huge, powerful neural network taught itself to recognize cats (and humans) in only three days.
Researchers from Google and Stanford working at Google’s secretive X Lab connected 16, 000 processors (still far short of a human’s estimated 80 billion neurons) and fed the neural network digital images extracted at random from 10 million YouTube videos. The machine was then left alone to learn what it could with no instruction on how to proceed. For three days, the network pored through the images, making connections and finding commonalities between objects. Next, the researchers attempted to see what the computer could identify from a list of 20, 000 items.
By learning from the most commonly occurring images the computer was able to achieve an 81.7% accuracy rating in identifying human faces, 76.7% accuracy at identifying human body parts, and 74.8 percent accuracy when identifying cats. The increases represent a 70% jump in accuracy compared to previous studies.
Source: Overload Stories
Electricity load forecasting based on Framelet Neural Network Technique.(Report): An article from: American Journal of Applied Sciences
Book (Science Publications)
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The resulting diffusion index and changes in the diffusion index are used to estimate the probit, logit and neural network forecasting models. The graph of the diffusion index from 1/1/2003 to 2/1/2013 is presented in Figure 1 below (in red - left axis).