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Duration 21 hours
Course Outline
Introduction to Multimodal AI and Ollama
- An overview of multimodal learning concepts
- Primary challenges in integrating vision and language
- Exploring the features and architecture of Ollama
Configuring the Ollama Environment
- Installation and configuration processes for Ollama
- Managing local model deployment
- Connecting Ollama with Python and Jupyter environments
Handling Multimodal Inputs
- Merging text and image data
- Including audio and structured data sources
- Creating effective preprocessing workflows
Applications for Document Understanding
- Pulling structured information from PDFs and images
- Pairing OCR technology with language models
- Constructing intelligent document analysis processes
Visual Question Answering (VQA)
- Preparing VQA datasets and benchmarking tools
- Training and assessing multimodal models
- Creating interactive VQA applications
Architecting Multimodal Agents
- Core principles of agent design using multimodal reasoning
- Synthesizing perception, language, and action
- Implementing agents for practical scenarios
Advanced Integration and Optimization
- Fine-tuning multimodal models via Ollama
- Enhancing inference performance
- Addressing scalability and deployment factors
Conclusions and Future Directions
Requirements
- A solid grasp of core machine learning principles
- Hands-on experience with deep learning frameworks like PyTorch or TensorFlow
- Working knowledge of natural language processing and computer vision
Target Audience
- Machine learning engineers
- AI researchers
- Product developers focused on integrating visual and textual workflows
