PRACTICAL DEEP LEARNING

Catalog description

A practical, code-first introduction to deep learning for real-world applications in vision, language, and tabular/time-series data. Students learn to fine-tune modern architectures (convolutional neural networks, transformers), use transfer learning, evaluate models with appropriate metrics, and deploy lightweight inference services. Emphasis is on applied problem-solving with industry datasets and clear communication of results to technical and business stakeholders. Prerequisites: COMP3125 or DATA3125 (4 credits) fall, summer