In machine learning (ML) and artificial intelligence (AI), a batch refers to a subset of the total dataset used during training or inference. Processing data in batches allows models to update their parameters incrementally, improving computational efficiency and stability. In inference, a group of images.
Applications/Use Cases:
- Training Efficiency: Using batches enables parallel processing, reducing the time required to train models on large datasets.
- Memory Management: Batches help manage memory usage by limiting the amount of data loaded into memory at any given time.
- Gradient Estimation: Batches provide a balance between the accuracy of gradient estimates and computational efficiency, leading to more stable training dynamics.
Selecting an appropriate batch size is crucial, as it can influence the model’s convergence rate and generalization performance.
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