# Adam

Adam solvers are the hassle free standard for optimizers.

**Most relevant hyper-parameters:**

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Intuition

The intuition behind Adam solvers is similar to the one behind SGD. The main difference is though, that Adam solvers are adaptive notifiers. Adam also adjusts the learning rate based on the gradients' magnitude using **Root Mean Square Propagation (RMSProp)**. This follows a similar logic as using momentum + dampening for SGD. This makes it robust for the non-convex optimization landscape of neural network.