Optimizer

An Optimizer is the algorithm responsible for updating a neural networkNeural NetworkA Neural Network is a computing system inspired by the structure of biological neural networks in the brain. It forms...'s parameters during training to minimize the loss functionLoss FunctionA Loss Function quantifies the difference between a model's predictions and the true values, guiding the training pro.... Starting from random or pretrained weights, the optimizer iteratively computes how each parameter should change to reduce prediction errors. The simplest optimizer, Stochastic Gradient Descent (SGD), updates parameters in the direction opposite to the gradient of the loss. Modern optimizers introduce adaptive learning rates, momentum, and other techniques to accelerate convergence and navigate the complex loss landscapes of deep neural networks. Popular Optimizers and Their CharacteristicsSGD with momentum accumulates past gradients to build velocity, smoothing noisy gradient estimates and accelerating convergence. Adam (Adaptive Moment Estimation) combines momentum with per-parameter adaptive learning rates, making it the default choice for many deep learning tasks due to its robust performance across architectures. AdamW adds weight decay regularizationRegularizationRegularization encompasses techniques that prevent machine learning models from overfitting to training data, ensurin... to Adam, improving generalizationGeneralizationGeneralization is the process of simplifying geographic features and reducing detail in spatial data to create maps a.... Learning rate schedulers like cosine annealing and warmup progressively adjust the learning rate during training. For geospatial models processing large satellite image datasets, the choice of optimizer affects both training speed and final model quality. Practical Considerations for Geospatial Model TrainingGeospatial AI training involves specific challenges that influence optimizer selection. Large batch sizes, often needed to efficiently process satellite image tiles on GPUs, require learning rate scaling and warmup strategies. Fine-tuning pretrained models for geospatial tasks typically benefits from lower learning rates than training from scratch, and different learning rates for pretrained versus new layers. Mixed precision training, supported by modern optimizers, accelerates processing of high-resolution satellite imagerySatellite ImagerySatellite imagery consists of photographs and data captured by Earth observation satellites orbiting the planet. Thes.... The optimizer interacts with regularization, learning rate schedules, and data augmentationData AugmentationData Augmentation expands training datasets through transformations like rotation, flipping, color shifting, and crop..., requiring holistic tuning of the entire training configuration.