Batch processing

Batch processing is a crucial technique in training. Instead of processing individual data samples one at a time, batch processing groups multiple samples into batches and processes them simultaneously. This approach offers several advantages, including improved computational efficiency and faster convergence.

Modern hardware, like GPUs, is optimized for parallel processing. By processing batches of data, these resources can be fully utilized, reducing training time significantly. Calculating gradients over a batch rather than a single sample provides a more accurate estimate of the true gradient, which leads to more stable and reliable updates during backpropagation. Also helps manage memory usage by controlling the amount of data loaded into memory at any given time. This is particularly important when working with large datasets.

• Small Batches: Offer noisy gradients, which can help escape local minima but might slow down convergence.
• Large Batches: Provide smoother gradients, leading to faster convergence but requiring more memory.