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Course Outline
Introduction to Multi-Modal AI
- What is multi-modal AI?
- Key challenges and applications.
- Overview of leading multi-modal models.
Text Processing and Natural Language Understanding
- Leveraging LLMs for text-based AI agents.
- Understanding prompt engineering for multi-modal tasks.
- Fine-tuning text models for domain-specific applications.
Image Recognition and Generation
- Processing images with AI: classification, captioning, and object detection.
- Generating images with diffusion models (Stable Diffusion, DALLE).
- Integrating image data with text-based models.
Speech and Audio Processing
- Speech recognition with Whisper ASR.
- Text-to-speech (TTS) synthesis techniques.
- Enhancing user interaction with voice-based AI.
Integrating Multi-Modal Inputs
- Building AI pipelines for processing multiple input types.
- Fusion techniques for combining text, image, and speech data.
- Real-world applications of multi-modal AI agents.
Deploying Multi-Modal AI Agents
- Building API-driven multi-modal AI solutions.
- Optimizing models for performance and scalability.
- Best practices for deploying multi-modal AI in production.
Ethical Considerations and Future Trends
- Bias and fairness in multi-modal AI.
- Privacy concerns with multi-modal data.
- Future developments in multi-modal AI.
Summary and Next Steps
Requirements
- A solid understanding of machine learning fundamentals.
- Experience with Python programming.
- Familiarity with deep learning frameworks (e.g., TensorFlow, PyTorch).
Audience
- AI developers.
- Researchers.
- Multimedia engineers.
21 Hours
