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コース概要
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
要求
- 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 時間
