Description: A training batch is a subset of data used in a training iteration within the machine learning process. This concept is fundamental in model training, as it allows large datasets to be divided into more manageable parts, thus facilitating the optimization and tuning of the model’s hyperparameters. Training batches enable the model to learn incrementally, updating its parameters based on the information contained in each batch. This not only improves training efficiency but also helps avoid issues like overfitting, as the model can generalize better when exposed to different subsets of data. Additionally, the batch size can influence the speed of model convergence and the quality of final predictions. In summary, training batches are a key tool in machine learning, allowing for a more structured and efficient approach to training complex models.
