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Duration 21 hours
Course Outline
Introduction to Multimodal AI and Ollama
- Foundations of multimodal learning
- Primary challenges in integrating vision and language models
- Examining the capabilities and architecture of Ollama
Configuring the Ollama Environment
- Installation and configuration of Ollama
- Strategies for local model deployment
- Connecting Ollama with Python and Jupyter environments
Handling Multimodal Inputs
- Techniques for integrating text and image data
- Incorporating audio streams and structured data formats
- Architecting effective preprocessing pipelines
Applications in Document Understanding
- Extracting structured data from PDFs and images
- Merging OCR technology with language models
- Creating intelligent workflows for document analysis
Visual Question Answering (VQA)
- Preparing VQA datasets and establishing benchmarks
- Training and assessing multimodal model performance
- Developing interactive VQA applications
Architecting Multimodal Agents
- Core principles of agent design leveraging multimodal reasoning
- Synthesizing perception, language processing, and action execution
- Deploying agents for specific real-world scenarios
Advanced Integration and Performance Optimization
- Performing fine-tuning on multimodal models via Ollama
- Enhancing inference speed and efficiency
- Addressing scalability and deployment best practices
Wrap-Up and Future Directions
Requirements
- A solid grasp of core machine learning concepts
- Hands-on experience with deep learning frameworks like PyTorch or TensorFlow
- Proficiency in natural language processing and computer vision techniques
Target Audience
- Machine learning engineers
- AI researchers
- Product developers integrating vision and text-based workflows
