👉 Adam is an adaptive optimization algorithm designed to address the limitations of traditional methods like Momentum and RMSProp in training deep neural networks. It combines the benefits of both by incorporating momentum, which helps accelerate gradients vectors in the right directions, thus improving convergence speed and stability, with the adaptive learning rate mechanism of RMSProp. Adam computes individual adaptive learning rates for each parameter, adjusting based on the first and second moments of the gradients. This means it maintains a running average of the gradients (similar to momentum) and the squared gradients (similar to RMSProp), then uses these to update the parameters. The algorithm's hyperparameters, such as the decay rates for the moving averages and the momentum coefficient, are designed to be small, leading to efficient and robust training across a wide range of deep learning tasks.
