The Adam method is a new way to optimize functions by using gradient information. It adapts to the data and parameters of the problem, works well with noisy or sparse gradients, and doesn't need much memory. It's easy to use and performs comparably to other methods in practice. The researchers also developed a variant called AdaMax, which is based on a different norm.