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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,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。 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。
Adam Optimizer 应该是最常用的优化算法,并且其已经在大量的深度神经网络实验上验证了其有效性,下面我将一步一步拆解,介绍Adam Optimizer的来龙去脉。 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。 作为一名算法工程师/AI研究者,如果问我哪个优化器是yyds,估计十有八九的人会脱口而出: Adam。 没错,Adam凭借其稳定性和易用性,多年来一直被奉为深度学习的“标配”。
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