Gradient Descent Batch Size

Gradient descent batch size, a hyperparameter in training deep learning models, significantly impacts model performance and efficiency. Current research explores optimal batch sizes across different model architectures and training environments, including investigations into adaptive methods that adjust batch size or learning rate per layer to improve convergence and generalization. These studies aim to enhance training efficiency and model accuracy, particularly in resource-constrained settings or when dealing with large datasets, ultimately contributing to more robust and scalable deep learning applications.