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In machine learning, the gradient descent (Gradient descent, GD) algorithm only needs to calculate the first derivative of the loss function, the calculation cost is small, it is very suitable for the training data very large application.The physical meaning of the gradient descent method is well understood, that is, the line is searched along the gradient direction of the current point, and the next iteration point is found. But, why does it derive t

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how services work2) Proficient in the system work principle (to this step already very few people can do, can filter out 80%)3) familiar with various distributions, RedHat Department (Fedora, CentOS), Debian (Ubuntu), Gentoo (Lfs,arch)4) Skilled script development, Perl,python,shell (three-choice two, or all), automation technology. ---The first gradient ends----1) The management thinking mode of the cluster can be cooperated by Puppet, script and other similar tools. 2) Extreme optimization of

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, more, the model convergence speed and performance will not be better, and sometimes there will be a decline.The experimental results in the paper 2a, the best or double model parallel, followed by collaborative distillation, the worst is unigram smooth0.9,label smooth 0.99 with the direct training performance is similar, after all, just a random noise.In addition, by comparing the co-distillation 2b of the same data with the collaborative sorting of

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