This reviews the 2015 paper which introduced Adam, an algorithm for first-order gradient-based optimization in scenarios involving stochastic objective functions. Adam uniquely computes adaptive learning rates for different parameters by estimating the first and second moments of gradients, offering advantages in computational efficiency and memory requirements. The paper details Adam's algorithm, its initialization bias correction technique, and analyzes its theoretical convergence properties, demonstrating a comparable regret bound to existing methods. Empirical results across various machine learning models, including logistic regression and neural networks, showcase Adam's practical effectiveness and robustness in large-scale, high-dimensional problems, even introduci