In this experiment, I explore the effect of varying learning rates on the performance of a simple fully connected neural network (FCN) model trained on [Fashion-MNIST dataset]. I tested three different learning rates: 0.001, 0.0005, and 0.0001, to understand how each rate affects training and validation performance

In terms of artifact management, logging models as wandb.Artifacts provided a great way to track versions and ensure reproducibility. It also allowed for easier comparison between different configurations, making it clear which model performed best under specific hyperparameters.