Batch

In the context of AI, particularly in ML, a batch refers to a subset of the dataset processed simultaneously during one iteration of forward and backward propagation within a neural network training cycle. Utilizing batches allows the model to take advantage of efficient matrix operations and can lead to faster convergence than processing one sample at a time (online learning) or the entire dataset (full-batch learning). Batched learning introduces less noisy gradient estimates compared to stochastic gradient descent, which can enhance the stability of the learning process. Additionally, it benefits from computational optimizations available in modern hardware like GPUs, which are adept at performing parallel computations that batches enable.

The concept of using batches in training ML models likely first came into casual use with the advent of deep learning in the mid-2010s. As neural network architectures grew in complexity and size, researchers realized that processing data in batches drastically reduced training time while maintaining model accuracy, thus gaining popularity during this period.

Key contributors to the development and popularization of batching techniques include prominent AI researchers and institutions focusing on neural networks and deep learning, such as Geoffrey Hinton, Yann LeCun, and Andrew Ng, whose work and teachings have widely disseminated the importance and implementation of batch processing in model training. Additionally, contributions from deep learning platforms like TensorFlow and PyTorch, developed by teams at Google Brain and Facebook AI Research respectively, have standardized the use of batches in training pipelines.