Weights & Biases (W&B) is a platform for tracking and visualizing machine learning experiments. It helps researchers and developers to log metrics, parameters, code, and model versions during training, making it easier to reproduce experiments, collaborate with team members, and identify the best-performing models. It is commonly used for experiment tracking, hyperparameter optimization, and model evaluation in deep learning and other machine learning tasks.
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