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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
- An 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
Testimonials (1)
Trainer responding to questions on the fly.
