I'm a beginner in Machine learning and i'm searching for some optimizer for the gradient descent. I've searched many topics about that, and did a state of art of all these optimizers. I have just one problem, and i can't figure it out. Don't judge me please, but i would like to know ?

Are we using ADAM optimizer alone or are we obliged to combine it with the SGD ? I didn't understand if it works alone or if it's here to optimize NOT the neural network but the SGD of the neural network?

Thank you for your help!

  • 2
    $\begingroup$ ADAM is a standalone optimizer, so no need to be combined with SGD. It is kind of advanced flavor of classical SGD. Have you seen this post: machinelearningmastery.com/…? I think it has all you need to know about ADAM, and it is even compared with others. $\endgroup$ Aug 12, 2018 at 18:25
  • $\begingroup$ Yep, as an aside, just about any optimizer you will come across is still a form of SGD, just with fancier handling of different weights and learning rate over time $\endgroup$
    – Sean Owen
    Nov 29, 2021 at 15:22

1 Answer 1


Adam optimization is an extension of stochastic gradient descent (SGD) optimization.

SGD maintains a single learning rate for all weight updates and the learning rate does not change during training.

Adam optimization can have a different learning rate for each weight and change the learning rate during training.


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