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Course Outline
Introduction to Mistral Multimodal Models
- Overview of Mistral Medium and its multimodal capabilities.
- OCR/document models and their use cases.
- Integration with open-source ecosystems.
OCR and Vision Pipelines
- OCR fundamentals using Mistral models.
- Preprocessing images and scanned documents.
- Extracting structured text from images.
Document Understanding
- Designing NLP pipelines for documents.
- Entity recognition, summarization, and classification.
- Cross-modal linking of text and vision data.
Search and Knowledge Applications
- Vision-text search systems.
- Building semantic search with OCR outputs.
- Enterprise document repositories.
Assistive and Interactive Applications
- UI design for multimodal assistants.
- Accessibility applications (e.g., vision-to-text).
- Real-world productivity tools.
Performance and Optimization
- Scaling multimodal pipelines.
- Inference performance tuning.
- Evaluating accuracy and efficiency trade-offs.
Case Studies and Future Directions
- Industry applications of multimodal AI.
- Research trends in OCR and document AI.
- Responsible AI considerations in vision-text tasks.
Summary and Next Steps
Requirements
- A solid understanding of natural language processing concepts.
- Hands-on experience with Python and machine learning frameworks.
- Familiarity with the fundamentals of computer vision.
Audience
- Product teams.
- Machine learning researchers.
- Applied machine learning engineers.
14 Hours
