Applied Deep Learning Tools and Deployment

This unit focuses on applying advanced deep learning tools, hardware acceleration, and deployment techniques to real-world problems such as vision, speech, forecasting, and game agents. Students will synthesize framework-specific workflows, optimize performance on GPUs and in the cloud, and consider safety, ethics, and workplace implications of AI systems.

Learning Objectives

• Analyze and compare deep learning frameworks, GPU acceleration strategies (CUDA, MXNet optimizations), and cloud deployment options for performance, scalability, and resource trade-offs
• Apply advanced model development and optimization techniques—including dynamic computation with PyTorch, distributed/cloud workflows, and tool-specific tuning—to accelerate training and inference for vision, speech, forecasting, and reinforcement learning tasks
• Demonstrate the integration and deployment of trained models using diverse toolchains (Deeplearning4j, Microsoft CNTK, Neural Designer, and other libraries) to produce reproducible pipelines, visualizations, and application prototypes
• Evaluate ethical, safety, privacy, and workplace considerations related to AI deployment (bias in demographic prediction, robustness in catastrophe forecasting, human-AI collaboration) and propose mitigation and governance strategies