Description: Adaptive Moment Estimation is an optimization algorithm used in training machine learning models, especially deep learning networks. This method is based on calculating adaptive learning rates for each model parameter, using estimates of the first and second moments of the gradients. In simple terms, the first moment refers to the mean of the gradients, while the second moment relates to the uncentered variance of these gradients. This duality allows the algorithm to adjust the learning rate individually for each parameter, resulting in a more efficient and robust optimization process. One of the most notable features of this approach is its ability to adapt to different scales of parameters, helping to avoid issues like overflow or underestimation of gradients. Additionally, Adaptive Moment Estimation is particularly useful in scenarios where data is noisy or exhibits high variability, as its design allows for more stable and faster convergence. In summary, this algorithm has become a fundamental tool in the field of deep learning, facilitating the training of complex models more effectively.
