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Course Outline
Foundations of Multi-Modal AI
- Defining multi-modal AI
- Core challenges and use cases
- Survey of prominent multi-modal models
Text Handling and Natural Language Comprehension
- Utilizing LLMs for text-centric AI agents
- Mastering prompt engineering for multi-modal tasks
- Adjusting text models for specific industry needs
Image Analysis and Creation
- AI-driven image processing: classification, captioning, and object detection
- Image generation using diffusion models (e.g., Stable Diffusion, DALLE)
- Merging image data with text-based models
Audio and Speech Handling
- Speech recognition via Whisper ASR
- Methods for text-to-speech (TTS) synthesis
- Improving user engagement through voice-driven AI
Combining Multi-Modal Inputs
- Constructing AI pipelines to handle diverse input types
- Fusion strategies for merging text, image, and speech data
- Practical applications of multi-modal AI agents
Deployment of Multi-Modal AI Agents
- Developing API-based multi-modal AI solutions
- Tuning models for optimal performance and scalability
- Key practices for implementing multi-modal AI in production environments
Ethical Implications and Emerging Trends
- Addressing bias and equity in multi-modal AI
- Privacy issues surrounding multi-modal data
- The future trajectory of multi-modal AI
Recap and Forward Path
Requirements
- A solid grasp of machine learning principles
- Proficiency in Python programming
- Working knowledge of deep learning frameworks (such as TensorFlow and PyTorch)
Target Audience
- AI developers
- Researchers
- Multimedia engineers
21 Hours
