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Duration 21 hours
Course Outline
Foundations of Multimodal AI and Ollama
- Introduction to multimodal learning concepts
- Primary challenges in integrating vision and language
- Overview of Ollama’s capabilities and architecture
Configuring the Ollama Environment
- Installation and configuration of Ollama
- Managing local model deployment
- Connecting Ollama with Python and Jupyter notebooks
Handling Multimodal Inputs
- Combining text and image data
- Including audio and structured data sources
- Designing effective preprocessing pipelines
Applications in Document Understanding
- Extracting structured information from PDFs and images
- Merging OCR technologies with language models
- Creating intelligent document analysis workflows
Visual Question Answering (VQA)
- Establishing VQA datasets and benchmarks
- Training and assessing multimodal models
- Developing interactive VQA applications
Architecting Multimodal Agents
- Core principles of agent design using multimodal reasoning
- Synthesizing perception, language, and action
- Deploying agents for practical use cases
Advanced Integration and Optimization
- Fine-tuning multimodal models via Ollama
- Enhancing inference performance
- Addressing scalability and deployment considerations
Wrap-up and Future Directions
Requirements
- A solid grasp of core machine learning principles
- Practical experience with deep learning frameworks like PyTorch or TensorFlow
- Working knowledge of natural language processing and computer vision
Intended Audience
- Machine learning engineers
- AI researchers
- Product developers integrating vision and text workflows
