What is an Adam Optimizer?

Detailed Deep Dive

The Adam (Adaptive Moment Estimation) optimizer is a widely used algorithm for training deep neural networks. It combines the principles of Momentum (which accelerates gradient descent by accumulating past gradients) and RMSProp (which scales the learning rate based on the running average of recent gradient magnitudes). By maintaining individual adaptive learning rates for each parameter, Adam provides robust, rapid convergence even on complex, non-convex loss surfaces, making it the default optimizer choice for most deep learning architectures.