Course Outline
Introduction to Multimodal AI for Government
- Overview of multimodal AI and its real-world applications for government operations
- Challenges in integrating text, image, and audio data within governmental systems
- State-of-the-art research and advancements relevant to public sector use cases
Data Processing and Feature Engineering for Government
- Handling text, image, and audio datasets in government contexts
- Preprocessing techniques tailored for multimodal learning in governmental applications
- Feature extraction and data fusion strategies for enhancing governmental AI systems
Building Multimodal Models with PyTorch and Hugging Face for Government
- Introduction to PyTorch for multimodal learning in government settings
- Utilizing Hugging Face Transformers for natural language processing (NLP) and vision tasks in governmental projects
- Combining different modalities into a unified AI model for government applications
Implementing Speech, Vision, and Text Fusion for Government
- Integrating OpenAI Whisper for speech recognition in governmental operations
- Applying DeepSeek-Vision for image processing in government contexts
- Fusion techniques for cross-modal learning to enhance governmental AI capabilities
Training and Optimizing Multimodal AI Models for Government
- Model training strategies tailored for multimodal AI in government applications
- Optimization techniques and hyperparameter tuning for improved governmental AI performance
- Addressing bias and improving model generalization to ensure fairness and accuracy in governmental systems
Deploying Multimodal AI in Real-World Applications for Government
- Exporting models for production use in government operations
- Deploying AI models on cloud platforms to support governmental services
- Performance monitoring and model maintenance to ensure reliable governmental AI systems
Advanced Topics and Future Trends in Multimodal AI for Government
- Zero-shot and few-shot learning techniques for enhancing multimodal AI in government settings
- Ethical considerations and responsible AI development for governmental applications
- Emerging trends in multimodal AI research with implications for government operations
Summary and Next Steps for Government
Requirements
- A solid understanding of machine learning and deep learning concepts for government applications
- Experience with AI frameworks such as PyTorch or TensorFlow
- Familiarity with processing text, image, and audio data
Audience
- AI developers for government projects
- Machine learning engineers
- Researchers
Testimonials (1)
Our trainer, Yashank, was incredibly knowledgeable. He modified the curriculum to match what we truly needed to learn, and we had a great learning experience with him. His understanding of the domain he was teaching was impressive; he shared insights from real experience and helped us solve actual problems we were facing in our work.
