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. 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. 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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