👉 Adam fluid is an adaptive learning rate optimization algorithm that combines the benefits of both AdaGrad and RMSProp. It uses a moving average of both the gradient and its squared values to compute an adaptive learning rate for each parameter. This approach helps in efficiently navigating the optimization landscape, particularly in scenarios with sparse gradients or non-stationary objectives. By adjusting the learning rate based on the historical gradient information, Adam fluid ensures faster convergence and better performance in training deep neural networks compared to traditional methods like stochastic gradient descent.
