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Duration 21 hours
Course Outline
Foundations of Multimodal AI and Ollama
- An overview of multimodal learning paradigms
- Primary challenges associated with integrating vision and language
- Examining the capabilities and internal architecture of Ollama
Establishing the Ollama Environment
- Installation and configuration of Ollama
- Strategies for local model deployment
- Connecting Ollama with Python and Jupyter notebooks
Handling Multimodal Inputs
- Merging text and image data streams
- Incorporating audio and structured data sources
- Architecting efficient preprocessing pipelines
Applications in Document Understanding
- Extracting structured insights from PDFs and images
- Linking OCR technologies 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-based applications
Architecting Multimodal Agents
- Core principles of agent design involving multimodal reasoning
- Synthesizing perception, language processing, and action execution
- Deploying agents for practical, real-world use cases
Advanced Integration and Performance Optimization
- Fine-tuning multimodal models via Ollama
- Enhancing inference speed and efficiency
- Addressing scalability and deployment strategies
Conclusion and Future Directions
Requirements
- A solid grasp of core machine learning concepts
- Proficiency 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 implementing vision and text integration workflows
