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Course Outline
Introduction to Multimodal AI
- Overview of multimodal AI and real-world applications
- Challenges in integrating text, image, and audio data
- State-of-the-art research and advancements
Data Processing and Feature Engineering
- Handling text, image, and audio datasets
- Preprocessing techniques for multimodal learning
- Feature extraction and data fusion strategies
Building Multimodal Models with PyTorch and Hugging Face
- Introduction to PyTorch for multimodal learning
- Using Hugging Face Transformers for NLP and vision tasks
- Combining different modalities in a unified AI model
Implementing Speech, Vision, and Text Fusion
- Integrating OpenAI Whisper for speech recognition
- Applying DeepSeek-Vision for image processing
- Fusion techniques for cross-modal learning
Training and Optimizing Multimodal AI Models
- Model training strategies for multimodal AI
- Optimization techniques and hyperparameter tuning
- Addressing bias and improving model generalization
Deploying Multimodal AI in Real-World Applications
- Exporting models for production use
- Deploying AI models on cloud platforms
- Performance monitoring and model maintenance
Advanced Topics and Future Trends
- Zero-shot and few-shot learning in multimodal AI
- Ethical considerations and responsible AI development
- Emerging trends in multimodal AI research
Summary and Next Steps
Requirements
- Strong understanding of machine learning and deep learning concepts
- Experience with AI frameworks like PyTorch or TensorFlow
- Familiarity with text, image, and audio data processing
Audience
- AI developers
- Machine learning engineers
- Researchers
21 Hours
