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Duration 21 hours
Course Outline
Introduction to Multimodal AI and Ollama
- Overview of multimodal learning concepts
- Primary challenges in integrating vision and language
- Functional capabilities and architectural design of Ollama
Configuring the Ollama Environment
- Installation and setup of Ollama
- Managing local model deployment strategies
- Connecting Ollama with Python and Jupyter environments
Managing Multimodal Data Inputs
- Integration of text and image data
- Including audio and structured data formats
- Designing effective preprocessing workflows
Applications in Document Comprehension
- Extracting structured data from PDFs and visual content
- Merging OCR processes with language models
- Constructing intelligent document analysis pipelines
Visual Question Answering (VQA)
- Establishing VQA datasets and evaluation benchmarks
- Training and assessing multimodal models
- Creating interactive VQA-based applications
Architecture of Multimodal Agents
- Core principles of designing agents for multimodal reasoning
- Unifying perception, language, and action components
- Implementing agents for practical real-world scenarios
Advanced Integration and Performance Optimization
- Fine-tuning multimodal models within Ollama
- Enhancing inference speed and efficiency
- Addressing scalability and deployment requirements
Conclusion and Future Directions
Requirements
- A solid grasp of fundamental machine learning principles
- Hands-on experience with deep learning frameworks like PyTorch or TensorFlow
- Knowledge of natural language processing and computer vision techniques
Target Audience
- Machine learning specialists
- AI researchers
- Product developers integrating visual and textual workflows
