Translations:Decoupled Weight Decay Regularization/25/en

AdamW has become the standard optimizer for a large fraction of contemporary deep learning, particularly for transformers in language and vision. Mainstream frameworks ship native implementations (torch.optim.AdamW in PyTorch since 1.2, tf.keras.optimizers.AdamW in TensorFlow/Keras), and the optimizer is the default in popular training stacks such as Hugging Face Transformers and timm. Practitioners typically tune AdamW with a small weight-decay coefficient (often around 0.01 to 0.1) and a cosine or linear-warmup learning-rate schedule, paralleling the AdamWR recipe.