Microbatch method

The Microbatch method is a training approach for neural networks that processes small groups of data points, called microbatches, rather than individual samples or large batches. This technique strikes a balance between efficiency and memory use, allowing the model to learn effectively without requiring extensive computational resources. By updating the model's parameters more frequently with smaller data chunks, microbatching can lead to faster convergence and more stable training, especially useful when hardware limitations prevent processing large batches at once. Overall, it's a practical method to optimize training speed and resource management in neural network development.