Bayesian Inference
Machine Learning
The number of training examples used in one iteration of model training.
Detailed Explanation
Batch size refers to the number of training samples processed simultaneously during one forward and backward pass in machine learning. It influences training speed, model convergence, and memory usage. A smaller batch size offers more frequent updates, potentially improving generalization, while larger batches leverage parallelism for faster training but may require more computational resources.
Use Cases
- •Use case: Tuning batch size to balance training speed and model accuracy for large neural networks.
Related Terms
Other terms in the Machine Learning category