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Course Outline
Introduction to Multimodal AI for Government
- Overview of multimodal AI and its applications in the public sector
- Challenges associated with integrating text, image, and audio data for government use
- State-of-the-art research and advancements relevant to government operations
Data Processing and Feature Engineering for Government
- Handling text, image, and audio datasets in a governmental context
- Preprocessing techniques tailored for multimodal learning in public sector workflows
- Feature extraction and data fusion strategies to enhance government applications
Building Multimodal Models with PyTorch and Hugging Face for Government
- Introduction to PyTorch, focusing on its application in multimodal learning for government
- Utilizing Hugging Face Transformers for natural language processing (NLP) and vision tasks in governmental projects
- Combining different modalities into a unified AI model for enhanced public sector solutions
Implementing Speech, Vision, and Text Fusion for Government
- Integrating OpenAI Whisper for speech recognition in governmental communications
- Applying DeepSeek-Vision for image processing in public sector applications
- Advanced fusion techniques to support cross-modal learning in government contexts
Training and Optimizing Multimodal AI Models for Government
- Model training strategies specifically designed for multimodal AI in the public sector
- Optimization techniques and hyperparameter tuning to improve governmental model performance
- Addressing bias and enhancing model generalization for government applications
Deploying Multimodal AI in Real-World Government Applications
- Exporting models for production use within governmental systems
- Deploying AI models on cloud platforms to support public sector operations
- Performance monitoring and model maintenance to ensure ongoing effectiveness in government settings
Advanced Topics and Future Trends for Government
- Exploring zero-shot and few-shot learning techniques in multimodal AI for government use
- Ethical considerations and responsible AI development practices for the public sector
- Emerging trends in multimodal AI research relevant to governmental needs
Summary and Next Steps for Government
Requirements
- A solid understanding of machine learning and deep learning principles for government applications
- Experience with artificial intelligence frameworks such as PyTorch or TensorFlow, tailored for government use
- Proficiency in processing text, image, and audio data, aligned with public sector standards
Audience
- AI developers for government projects
- Machine learning engineers supporting government initiatives
- Researchers focused on government applications
21 Hours
