Course Outline
Introduction to Multimodal AI
- Overview of multimodal AI and its real-world applications
- Challenges associated with integrating text, image, and audio data
- Current state-of-the-art research and advancements
Data Processing and Feature Engineering
- Managing text, image, and audio datasets
- Preprocessing techniques tailored for multimodal learning
- Feature extraction and data fusion strategies
Constructing Multimodal Models with PyTorch and Hugging Face
- Introduction to PyTorch for multimodal learning
- Utilizing Hugging Face Transformers for NLP and vision tasks
- Integrating diverse modalities into 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
- Strategies for training multimodal AI models
- Optimization techniques and hyperparameter tuning
- Addressing bias and enhancing model generalization
Deploying Multimodal AI in Real-World Applications
- Exporting models for production environments
- 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
- Proficient understanding of machine learning and deep learning concepts
- Prior experience with AI frameworks such as PyTorch or TensorFlow
- Familiarity with processing text, image, and audio data
Target Audience
- AI developers
- Machine learning engineers
- Researchers
Testimonials (1)
Our trainer, Yashank, was incredibly knowledgeable. He modified the curriculum to match what we truly needed to learn, and we had a great learning experience with him. His understanding of the domain he was teaching was impressive; he shared insights from real experience and helped us solve actual problems we were facing in our work.
