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. 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. In a bas library special collection of articles, learn about a controversial interpretation of the creation of woman, and explore other themes related to adam
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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.
