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Duration 21 hours
Course Outline
Introduction to Multi-Modal AI
- Defining the concept of multi-modal AI
- Exploring primary challenges and practical use cases
- Surveying prominent multi-modal models
Text Processing and Natural Language Understanding
- Harnessing LLMs for text-centric AI agents
- Mastering prompt engineering for multi-modal contexts
- Adapting text models for specialized domain applications
Image Recognition and Generation
- AI-driven image processing: classification, captioning, and object detection
- Creating images using diffusion models (Stable Diffusion, DALLE)
- Merging image data with text-based architectures
Speech and Audio Processing
- Executing speech recognition via Whisper ASR
- Applying Text-to-speech (TTS) synthesis methods
- Improving user engagement through voice-activated AI
Integrating Multi-Modal Inputs
- Developing AI pipelines that manage diverse input types
- Applying fusion techniques to combine text, image, and speech streams
- Examining real-world implementations of multi-modal agents
Deploying Multi-Modal AI Agents
- Creating API-driven multi-modal AI solutions
- Tuning models for peak performance and scalability
- Adhering to best practices for production-level deployment
Ethical Considerations and Future Trends
- Addressing bias and fairness within multi-modal systems
- Navigating privacy issues associated with multi-modal data
- Looking ahead at future advancements in multi-modal AI
Conclusion and Future Directions
Requirements
- A solid grasp of machine learning fundamentals
- Practical experience with Python programming
- Proficiency with deep learning frameworks, such as TensorFlow and PyTorch
Target Audience
- AI developers
- Researchers
- Multimedia engineers
