Applied Deep Learning Solutions

This capstone unit builds on core deep learning principles to apply, optimize, and deploy models across real-world domains. Students will accelerate models with GPU tools, integrate frameworks into cloud and enterprise workflows, develop domain-specific solutions (vision, speech, forecasting, design), and address safety, ethics, and usability concerns.

Learning Objectives

• Apply GPU acceleration and framework-specific optimization techniques (e.g., CUDA, MXNet, PyTorch, CNTK) and analyze performance metrics (training time, throughput, GPU utilization) to produce measurable improvements over baseline implementations.
• Demonstrate deployment of trained models into cloud or enterprise environments by containerizing or exposing models as services using appropriate frameworks (e.g., Deeplearning4j) and documenting a scalable, reproducible deployment workflow.
• Analyze real-world datasets (image, audio, tabular, time series) to design, train, and evaluate domain-specific models — including voice generation, image-to-art, demographic prediction, solar-savings estimation, building design, and catastrophe forecasting — and present results with clear visualizations and reproducible code.
• Evaluate ethical, safety, and workplace considerations by auditing models for bias, privacy, robustness, and accessibility; implement and document mitigations, code comments, and best-practice reproducibility steps to improve trust and safety.