Course Outline
Introduction to Multimodal AI
- Overview of multimodal AI and its real-world applications.
- Challenges involved in integrating text, image, and audio data.
- State-of-the-art research and recent advancements.
Data Processing and Feature Engineering
- Working with text, image, and audio datasets.
- 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.
- Utilizing Hugging Face Transformers for NLP and vision tasks.
- Combining different modalities into a unified AI model.
Implementing Speech, Vision, and Text Fusion
- Integrating OpenAI Whisper for speech recognition.
- Applying DeepSeek-Vision for image processing.
- Techniques for fusion in 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.
- Monitoring performance and maintaining models.
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 robust understanding of machine learning and deep learning concepts.
- Practical experience with AI frameworks such as PyTorch or TensorFlow.
- Familiarity with the processing of 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.
