The Adam optimizer, short for Adaptive Moment Estimation, is a widely used optimization algorithm in machine learning and deep learning. It combines the advantages of two other popular optimization techniques: RMSProp and Stochastic Gradient Descent (SGD) with momentum. This combination allows Adam to adaptively adjust the learning rates for each parameter, which can lead to faster convergence during training.

One of the key features of Adam is its use of moving averages of both the gradients and the squared gradients. This helps in stabilizing the updates and allows the optimizer to perform well even in scenarios with sparse gradients. The algorithm is particularly popular due to its effectiveness and ease of use, making it a go-to choice for many practitioners in the field ²⁴.

In practice, Adam has been recognized for its performance in various machine learning competitions, including those on platforms like Kaggle, where it has been a common choice among winning solutions ⁴⁵.