This report explores the impact of varying learning rates on CNN model performance using Weights & Biases (W&B). By leveraging W&B's robust tracking and visualization tools, the analysis clearly demonstrates how different learning rates affect accuracy and loss, enabling rapid convergence and effective weight updates. The seamless management of training artifacts—model versions and logs—ensures reproducibility and facilitates continuous improvement, underscoring the significant benefits of using W&B in