Course Outline

Introduction to Multimodal AI

  • Overview of DeepSeek’s multimodal capabilities
  • Understanding cross-modal learning and applications
  • Challenges and advantages of multimodal AI

Text Processing with DeepSeek

  • Advanced text generation and analysis
  • Fine-tuning DeepSeek for text-based AI models
  • Sentiment analysis and natural language understanding

Image Analysis with DeepSeek

  • DeepSeek Vision for image recognition and analysis
  • Generating and enhancing images with AI
  • Combining image and text for AI-driven applications

Audio Processing with DeepSeek

  • Using DeepSeek for speech recognition and synthesis
  • Audio feature extraction and processing techniques
  • Integrating voice AI with text and image models

Building Cross-Modal AI Applications

  • Combining text, image, and audio in a single AI workflow
  • Developing multimodal AI chatbots and assistants
  • Case studies of multimodal AI in various industries

Optimizing and Fine-Tuning Multimodal AI Models

  • Performance optimization techniques for multimodal AI
  • Reducing latency and improving inference efficiency
  • Deploying multimodal AI applications at scale

Future of Multimodal AI and DeepSeek

  • Emerging trends in cross-modal AI applications
  • DeepSeek’s roadmap for multimodal AI advancements
  • Opportunities for innovation in multimodal AI

Summary and Next Steps

Requirements

  • Basic knowledge of machine learning and deep learning
  • Experience with Python and AI frameworks
  • Familiarity with text, image, or audio processing

Audience

  • AI researchers developing multimodal AI applications
  • Developers integrating DeepSeek for advanced AI use cases
  • Data scientists working on cross-modal learning
 14 Hours

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