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Keras Deep Learning -- The basis of neural networks

Neural networks are powerful learning algorithms inspired by the way the brain works. Similar to the way neurons connect to each other in the brain, a neural network takes input, passes it through the network through some function, and some of the neurons connected behind it are activated to produce output.

NASH: based on the rich network of morphism and mountain climbing algorithm of neural network architecture search | ICLR 2018

The baseline method is provided, and the randomly constructed network is trained with SGDR. The error rate of ciFAR-10 can reach 6%-7%, which is higher than most NAS methods. EAS extends the research on network morphisms and can provide popular network construction blocks, such as Skip Connection and BN. ...

"Artificial Intelligence Sharing" in Xiamen GDG 2018

A horde of eager GDgers. At 9:00, The activity officially began. Yang Qiang, Deputy Secretary of Information School of Xiamen University, xu Chunhang, Deputy Director of software Park Management Office of Torch Park Management Committee, gave a short speech. Zheng Lingxiang and Shan Linjie introduced GDG DevFest, Sino-US Young Maker Exchange Center, Xiamen Google Developer Community, and Mais Plan respectively. And Women Techmakers...

Deep Learning 005- Solving dichotomies in a few lines of Keras code

Many articles and textbooks use the MNIST dataset as the "Hello World" program for deep learning, but this dataset has one big feature: it is a typical multi-classification problem (there are 10 categories in total). When we first started to get into deep learning, I thought we should start with the simplest dichotomy problem. In terms of deep learning frameworks, the popular one is Tens...

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