Stanford CS230 | Autumn 2025 | Lecture 3: Full Cycle of a DL project

Andrew Ng delivers a lecture about the full cycle of a deep learning project, emphasizing the iterative nature of machine learning development due to the unpredictable nature of data. He uses the example of building a face recognition system to illustrate the different stages, including problem specification, data collection, model design, training, deployment, and maintenance. Ng stresses the importance of rapid experimentation and adaptation, advocating for quick data collection and model iteration to identify and address issues efficiently. He also touches on the challenges of data drift, the value of simple models for robustness, and the necessity of monitoring model performance in real-world deployments, highlighting the need for machine learning engineers to focus on building systems that work in practice rather than just performing well on test sets. The lecture includes interactive Q&A, addressing data collection strategies, the balance between data quality and speed, and the relevance of training data distribution.