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Duration 21 hours
Course Outline
Foundations of Multimodal AI and Ollama
- Core concepts of multimodal learning
- Primary challenges in combining vision and language
- Technical capabilities and architectural design of Ollama
Configuring the Ollama Ecosystem
- Installation and configuration procedures for Ollama
- Strategies for local model deployment
- Connecting Ollama with Python and Jupyter environments
Handling Multimodal Data Inputs
- Merging text and image data streams
- Including audio and structured data formats
- Architecting effective preprocessing pipelines
Applications in Document Understanding
- Extracting structured data from PDFs and visual content
- Synthesizing OCR techniques with language models
- Constructing intelligent document analysis workflows
Visual Question Answering (VQA)
- Establishing VQA datasets and evaluation benchmarks
- Training and assessing multimodal model performance
- Developing interactive VQA-driven applications
Architecture of Multimodal Agents
- Core principles of agent design involving multimodal reasoning
- Unifying perception, language processing, and action execution
- Implementing agents for practical real-world scenarios
Advanced Integration and Performance Tuning
- Performing fine-tuning of multimodal models via Ollama
- Enhancing inference speed and efficiency
- Addressing scalability and deployment strategies
Conclusion and Future Pathways
Requirements
- A solid grasp of fundamental machine learning principles
- Practical experience with deep learning frameworks like PyTorch or TensorFlow
- Knowledge in natural language processing and computer vision domains
Target Audience
- Machine learning engineers
- AI researchers
- Product developers integrating vision and text-based workflows
