The chapters in this book emphasize applied deep learning systems for forecasting, diagnostics, telemetry enhancement, and predictive maintenance. Because these models are increasingly used to support operational decisions, a concise theoretical framing is useful to clarify what the models learn, what they do not learn, and how their outputs should be interpreted in an engineering workflow. This section defines the core learning setups used in the book, outlines inherent limitations that commonly appear in drilling datasets, and summarizes interpretability expectations that align with safety and reliability requirements in field operations.