优化器

简介：
The optimizer plays a crucial role in guiding the learning process of machine learning models. It dynamically adjusts the model’s parameters during training, aiming to minimize a specified loss function and ultimately improve the model’s predictive accuracy. Specifically, the optimizer calculates gradients – representing the sensitivity of the loss function with respect to each parameter – and then utilizes these gradients to update the parameters in a direction that reduces the loss. Different optimization algorithms, such as stochastic gradient descent (SGD) and Adam, employ varying strategies for parameter updates, each with its own strengths and weaknesses depending on the specific problem being addressed. Choosing an appropriate optimizer is therefore a critical step in building successful machine learning systems.