AdamW is an optimization algorithm used in training machine learning models, particularly in deep learning. It is an extension of the Adam optimizer, which combines the benefits of adaptive learning rates and momentum. The key difference with AdamW is the way it applies weight decay, a regularization technique that helps prevent overfitting by penalizing large weights. In AdamW, weight decay is decoupled from the gradient updates, allowing for more effective regularization. This results in better generalization performance in many tasks, making it a popular choice among researchers and practitioners in the field of artificial intelligence and machine learning.