A data pipeline for neural network training refers to a series of steps that transform raw data into a format suitable for training. The process includes data collection, preprocessing, augmentation, and loading.
The pipeline begins with acquiring data, followed by cleaning (removing noise or outliers), normalization (scaling features), and augmentation (introducing variability). Augmentation techniques like rotating or flipping images increase data diversity without collecting more data.
Finally, the processed data is fed into the neural network in batches for training. A well-optimized pipeline ensures efficient data handling and improves the training process, making it faster and more scalable.