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Duration 21 hours
Course Outline
Foundations of Multi-Modal AI
- Defining the concept of multi-modal AI
- Exploring core challenges and practical applications
- Surveying leading multi-modal models
Text Processing and Natural Language Understanding
- Utilizing LLMs to power text-based AI agents
- Mastering prompt engineering for multi-modal tasks
- Fine-tuning text models for specific domain requirements
Image Recognition and Generation
- AI-driven image handling: classification, captioning, and object detection
- Image generation using diffusion models, including Stable Diffusion and DALLE
- Merging image data with text-based architectures
Speech and Audio Processing
- Speech recognition via Whisper ASR
- Techniques for text-to-speech (TTS) synthesis
- Improving user engagement through voice-centric AI
Integrating Multi-Modal Inputs
- Constructing AI pipelines capable of handling diverse input streams
- Fusion strategies for unifying text, image, and speech data
- Examining real-world uses of multi-modal AI agents
Deploying Multi-Modal AI Agents
- Developing API-centric multi-modal AI solutions
- Tuning models for optimal performance and scalability
- Best practices for production-grade deployment
Ethical Considerations and Future Trends
- Addressing bias and fairness in multi-modal AI
- Navigating privacy implications of multi-modal data
- Emerging trends and future trajectories in the field
Recap and Path Forward
Requirements
- A solid grasp of fundamental machine learning concepts
- Proficiency in Python programming
- Knowledge of deep learning frameworks, such as TensorFlow and PyTorch
Target Audience
- AI Developers
- Researchers
- Multimedia Engineers
