適応モーメント推定法（一般にAdamとして知られる）は、高度な 最適化アルゴリズム widely used in 機械学習モデルのトレーニング, particularly in 深層学習. Introduced in 2014 by D.P. Kingma and J.B. Ba, Adam combines the ideas of two other popular 最適化手法: モーメンタム and RMSProp.
Adamの主な利点は、その適応的に調整できる能力にあります 学習率 for each parameter based on the first and second moments of the gradients. This means that during training, Adam tracks both the average of the gradients (first moment) and the variance of the gradients (second moment). By doing so, it can make more informed updates to the model parameters, allowing for faster convergence and improved performance.
Adamは2つの hyperparameters, beta1 and beta2, which control the decay rates of the moving averages of the gradients and their squared values, respectively. Typically, beta1 is set to 0.9 and beta2 to 0.999. Another crucial parameter is epsilon, a small constant added to prevent division by zero during updates.
One of the key features of Adam is its robustness to noisy gradients and its efficiency in handling sparse data. This makes it particularly suitable for applications in 自然言語処理, image recognition, and other fields where gradient noise is prevalent. Overall, Adam has become a go-to choice for many practitioners due to its simplicity and effectiveness in optimizing complex models.