Multi-Modal AI Agents: Integrating Text, Image, and Speech Training Course
Multi-modal AI agents are transforming human-computer interaction by integrating text, images, speech, and video processing capabilities.
This instructor-led, live training (online or onsite) is aimed at intermediate-level to advanced-level AI developers, researchers, and multimedia engineers who wish to build AI agents capable of understanding and generating multi-modal content.
By the end of this training, participants will be able to:
- Develop AI agents that process and integrate text, image, and speech data.
- Implement multi-modal models such as GPT-4 Vision and Whisper ASR.
- Optimize multi-modal AI pipelines for efficiency and accuracy.
- Deploy multi-modal AI agents in real-world applications.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
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
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