Description: Dynamic batch size is a concept in the field of machine learning model training that refers to the ability to adjust the number of data samples processed in each iteration of training. Unlike a fixed batch size, where a constant number of examples is used at each step, the dynamic batch size can vary based on available computational resources, model complexity, or the behavior of the optimization algorithm. This flexibility allows for better utilization of memory and processing resources, potentially resulting in more efficient and faster training. Additionally, dynamic batch size can help mitigate issues like overfitting, as it enables the model to better adapt to variations in the data. In practice, this means that during training, the system can increase or decrease the number of examples processed simultaneously, thus optimizing the model’s performance and convergence. This approach is particularly useful in environments where resources are limited or when working with large volumes of data, allowing for a balance between training speed and the quality of the final model.
