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

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