What are the common choices for optimizers (e.g., Adam, RMSprop)?

Common choices for optimizers in machine learning include Adam, RMSprop, SGD (Stochastic Gradient Descent), Adagrad, and Adadelta. These algorithms adjust model parameters during training to minimize the loss function, each with distinct approaches to balancing speed and stability. Adam and RMSprop are widely adopted due to their adaptive learning rate mechanisms, which automatically adjust step sizes for each parameter. SGD, while simpler, remains a baseline option, often enhanced with momentum or learning rate schedules. The choice depends on factors like dataset size, model architecture, and the need for fine-grained control.

Adam (Adaptive Moment Estimation) combines ideas from RMSprop and momentum-based SGD. It maintains per-parameter learning rates by computing adaptive estimates of first-order (mean) and second-order (uncentered variance) moments of the gradients. This makes it robust to sparse gradients and suitable for problems with noisy or complex loss landscapes. RMSprop, developed to address Adagrad’s diminishing learning rates, divides the learning rate by an exponentially decaying average of squared gradients. It works well for non-stationary objectives, such as recurrent neural networks (RNNs). SGD with momentum accelerates convergence by accumulating a velocity vector in directions of persistent reduction, while vanilla SGD applies updates proportional to the current gradient.

Developers often choose Adam as a default due to its strong empirical performance across tasks like image classification and natural language processing. Frameworks like TensorFlow and PyTorch implement it with recommended hyperparameters (e.g., learning rate 0.001). RMSprop is preferred for RNNs or when training stability is critical. SGD with momentum or learning rate decay is still used in scenarios requiring precise control, such as fine-tuning pretrained models. Adagrad suits sparse data (e.g., NLP embeddings) but may require careful tuning to avoid early convergence. Experimentation is key: smaller networks might train faster with SGD, while complex models often benefit from Adam’s adaptability. Always monitor training curves to detect issues like oscillations or stagnation, which may signal a need to switch optimizers or adjust hyperparameters.