Batch Size

Batch size refers to the number of training examples utilized in one iteration of the training process in machine learning and deep learning. It significantly affects the model's training efficiency, convergence speed, and overall performance. A smaller batch size often leads to more accurate models due to more frequent updates to the weights, but it requires more iterations to complete an epoch. Conversely, larger batch sizes can speed up training but may lead to less accurate models due to fewer updates. Common use cases include optimizing neural network training in frameworks like TensorFlow and PyTorch.