Dive into the second part of a comprehensive three-part workshop on deep learning for satellite imagery. Learn to train deep learning models, debug locally, and make predictions with performance evaluations. Explore the U-Net model architecture, implement custom loss functions, and master model metrics. Practice code debugging, model training, and prediction techniques. Discover how to save and export models, create network plots, and use diagnostic callbacks. Visualize your model with Netron and access the source code on GitHub for further study. Perfect for those looking to enhance their skills in applying deep learning to satellite image analysis.