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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

  • A solid 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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