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Exponentially accelerated architecture search: CMU proposes a differentiable architecture search method based on gradient descent

Adapted from arXiv by Hanxiao Liu, Karen Simonyan and Yiming Yang. According to the researchers, this method has been proven to achieve industry-best results on both convolutional and recurrent neural networks, and the GPU computing power is sometimes only 700 times that of previous search methods, meaning that a single GPU can complete the task. The theory of the study...

Instead of advanced principles, let's talk about machine learning in simple language

@toc ✨ Blogger introduction ?️ Introduction Machine learning everyone is talking about it, but apart from teachers who know what it is, very few people can explain what it is. If you read online articles about machine learning, you're likely to come across one of two things: a thick academic trilogy filled with theorems (I got it

"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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