Adaptive Moment Estimation (Adam) is an optimization algorithm commonly used in machine learning and deep learning. It is particularly useful for training artificial neural networks. Adam combines the advantages of two other popular optimization algorithms: AdaGrad and RMSProp.


The main idea behind Adam is to dynamically adapt the learning rate during the training process. This adaptive learning rate allows the algorithm to converge faster and more efficiently compared to traditional optimization methods.


Adam calculates individual learning rates for each parameter in the neural network by considering both the average of past gradients and their variances. This way, the algorithm can make larger updates for parameters with smaller gradients and smaller updates for parameters with larger gradients. In other words, Adam automatically adjusts the learning rate based on the sensitivity of each parameter, leading to faster convergence and better performance.


One of the key features of Adam is its ability to handle sparse gradients effectively. It achieves this by incorporating bias correction terms, ensuring that the algorithm performs well even when dealing with sparse data.


Overall, Adam has become a popular choice for training neural networks due to its adaptability and efficiency. It has been widely adopted in various deep learning frameworks, such as TensorFlow and PyTorch, as a default optimization algorithm.


In summary, Adaptive Moment Estimation (Adam) is an optimization algorithm that dynamically adjusts the learning rate during the training process. By considering both the average of past gradients and their variances, Adam allows for faster convergence and better performance in training artificial neural networks.