Course Outline
Introduction to Multimodal Artificial Intelligence
- Overview of multimodal systems and their utility in government operations for government
- Challenges associated with the integration of text, visual, and audio data streams
- Current state of research and technological advancements
Data Processing and Feature Engineering
- Management of multimodal datasets including text, image, and audio components
- Preprocessing methodologies for effective multimodal learning environments
- Strategies for feature extraction and data fusion
Development of Multimodal Models Using PyTorch and Hugging Face
- Fundamentals of utilizing PyTorch for multimodal learning applications
- Application of Hugging Face Transformers for natural language processing and computer vision tasks
- Methodologies for unifying disparate modalities within a single AI architecture
Implementation of Speech, Vision, and Text Fusion
- Integration of OpenAI Whisper for automated speech recognition capabilities
- Utilization of DeepSeek-Vision for advanced image processing functions
- Techniques for enabling cross-modal learning and information fusion
Training and Optimization of Multimodal AI Models
- Strategic approaches to training multimodal artificial intelligence systems
- Optimization protocols and hyperparameter tuning procedures
- Mitigation of bias and enhancement of model generalization for accurate decision support
Deployment of Multimodal AI in Operational Environments
- Procedures for exporting models to production-ready formats
- Deployment of AI systems across cloud infrastructure platforms
- Protocols for performance monitoring and ongoing model maintenance
Advanced Topics and Future Trends
- Application of zero-shot and few-shot learning techniques in multimodal contexts
- Ethical guidelines and responsible development practices for AI systems
- Emerging trends shaping the future of multimodal AI research and governance
Summary and Next Steps
Requirements
- Comprehensive knowledge of machine learning and deep learning principles
- Practical experience with artificial intelligence frameworks such as PyTorch or TensorFlow
- Proficiency in processing text, image, and audio datasets
Audience
- AI developers
- 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.
