Building Custom Multimodal AI Models with Open-Source Frameworks Training Course
Multimodal AI integrates multiple data types, such as text, images, and audio, to enhance machine learning models and applications.
This instructor-led, live training (online or onsite) is aimed at advanced-level AI developers, machine learning engineers, and researchers who wish to build custom multimodal AI models using open-source frameworks.
By the end of this training, participants will be able to:
- Understand the fundamentals of multimodal learning and data fusion.
- Implement multimodal models using DeepSeek, OpenAI, Hugging Face, and PyTorch.
- Optimize and fine-tune models for text, image, and audio integration.
- Deploy multimodal AI models in real-world applications.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
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
Open Training Courses require 5+ participants.
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