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正因为Adam是深度学习时代最有影响力的工作之一，该如何（定量地）理解它就是一个非常重要、非常困难、又非常迷人的挑战。 adam算法是一种基于“momentum”思想的随机梯度下降优化方法，通过迭代更新之前每次计算梯度的一阶moment和二阶moment，并计算滑动平均值，后用来更新当前的参数。 如果想使训练深层网络模型快速收敛或所构建的神经网络较为复杂，则应该使用Adam或其他自适应学习速率的方法，因为这些方法的实际效果更优。
Adam算法是在2014年提出的一种基于一阶梯度的优化算法，它结合了 动量 （Momentum）和 RMSprop （Root Mean Square Propagation）的思想， 自适应地调整每个参数的学习率。 Adam（Adaptive momentum）是一种自适应动量的随机优化方法（A method for stochastic optimization），经常作为 深度学习 中的优化器算法。 在 PyTorch 里， Adam 和 AdamW 的调用语法几乎一模一样，这是因为 PyTorch 的优化器接口是统一设计的，使用方式都继承自 torch.optim.Optimizer 的通用结构。
Adam，这个名字在许多获奖的 Kaggle 竞赛中广为人知。 参与者尝试使用几种优化器（如 SGD、Adagrad、Adam 或 AdamW）进行实验是常见的做法，但真正理解它们的工作原理是另一回事。
Adam优化器凭借其独特的设计和出色的性能，已成为深度学习领域不可或缺的工具。 深入理解其原理和性质，能帮助我们更好地运用它提升模型训练效果，推动深度学习技术不断发展。 AdamW目前是大语言模型训练的默认优化器，而大部分资料对Adam跟AdamW区别的介绍都不是很明确，在此梳理一下Adam与AdamW的计算流程，明确一下二者的区别。 2014年12月， Kingma和Lei Ba两位学者提出了Adam优化器，结合AdaGrad和RMSProp两种优化算法的优点。 对梯度的一阶矩估计（First Moment Estimation，即梯度的均值）和二阶矩估计（Second Moment Estimation，即梯度的未中心化的方差）进行综合考虑，计算出更新步长。
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