Adam style

Adam (short for Adaptive Moment Estimation) is an optimization algorithm used in training machine learning models. It adjusts the model's parameters efficiently by combining ideas from other methods: it keeps track of the average of past gradients (the direction the model is learning) and the average of their squares. This helps Adam respond to different learning scenes by adapting step sizes for each parameter, leading to faster and more reliable training. Essentially, Adam balances learning speed and stability, making it popular for training complex models like neural networks.