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Adam optimization algorithm (adam gradient descent optimizer, agdo) is a new meta-heuristic algorithm (intelligent optimization algorithm). The author was inspired by the adam optimizer and used three rules to explore the entire search process: asymptotic gradient momentum product. The adam and eve story states that god formed adam out of dust, and then eve was created from one of adam’s ribs. The Adam algorithm is an optimization algorithm based on first-order gradients proposed in 2014. It combines the ideas of momentum and rmsprop (root mean square propagation) to adaptively adjust the learning rate of each parameter.
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Thank you for the invitation. In addition to talking about adam here, I also want to help you solve the problem of not understanding the article. If you can’t understand articles and papers, there are usually three reasons: Poor mastery of prerequisite knowledge. Failure to combine theory and practice. Failure to understand the image of knowledge. Adam is practical in nature. Was it really his rib? Adam: The adam optimization algorithm basically combines momentum and rmsprop. We have already learned about momentum and rmsprop before, so now we directly give the update strategy of adam, ==adam algorithm is combined.
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The story of adam, eve, and the fall forms the underpinning of almost all of our understanding of men and women, making it perhaps the most important theme from the bible. 3. The basic mechanism of the adam optimization algorithm The adam algorithm is different from the traditional stochastic gradient descent. Stochastic gradient descent maintains a single learning rate (i.e. alpha) to update all weights, and the learning rate does not change during the training process. And adam goes through the calculation ladder.
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