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 recent advancements.
Data Processing and Feature Engineering
- Managing datasets for text, images, and audio.
- Preprocessing techniques tailored for multimodal learning.
- Strategies for feature extraction and data fusion.
Building Multimodal Models with PyTorch and Hugging Face
- Introduction to PyTorch for multimodal learning applications.
- Utilizing Hugging Face Transformers for NLP and vision tasks.
- Integrating different modalities into a unified AI model.
Implementing Speech, Vision, and Text Fusion
- Incorporating OpenAI Whisper for speech recognition.
- Applying DeepSeek-Vision for image processing tasks.
- Techniques for fusing cross-modal data.
Training and Optimizing Multimodal AI Models
- Strategies for training multimodal AI models.
- Optimization methods 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
- A solid understanding of machine learning and deep learning concepts.
- 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.
