Optimizer

An optimizer is an algorithm or technique used in machine learning and deep learning to tune a model's parameters (weights and biases) toward their optimal values. During the training process, the optimizer adjusts these parameters to minimize the loss function, thereby improving prediction accuracy. Commonly used optimizers include: • SGD (Stochastic Gradient Descent) A fundamental gradient descent method — simple and efficient for parameter updates • Momentum Incorporates the influence of past gradients to accelerate and stabilize learning • AdaGrad Automatically adjusts the learning rate for parameters that are updated frequently • RMSprop An improvement on AdaGrad that dynamically adjusts the learning rate for more stable convergence • Adam (Adaptive Moment Estimation) Combines the benefits of Momentum and RMSprop; the most widely used optimizer, delivering strong performance across many tasks The choice of optimizer is a critical hyperparameter that significantly affects training speed, convergence stability, and final model performance. Selecting and tuning the right optimizer is key to efficient model training. Optimizers are used not only in neural networks but broadly across the training of general machine learning models.