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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
- Core capabilities and architectural overview of Ollama
Configuring the Ollama Environment
- Installation and configuration processes for Ollama
- Managing local model deployment workflows
- Connecting Ollama with Python and Jupyter notebooks
Handling Multimodal Inputs
- Synchronizing text and image data streams
- Integrating audio and structured data formats
- Architecting effective preprocessing pipelines
Applications in Document Understanding
- Extracting structured data from PDFs and visual content
- Enhancing OCR by combining it with language models
- Creating intelligent workflows for document analysis
Visual Question Answering (VQA)
- Establishing VQA datasets and performance benchmarks
- Training and assessing multimodal model performance
- Developing interactive VQA-based applications
Designing Multimodal Agents
- Core principles of agent design featuring multimodal reasoning
- Uniting perception, language processing, and action execution
- Deploying agents for practical, real-world scenarios
Advanced Integration and Optimization
- Refining multimodal models through fine-tuning with Ollama
- Enhancing inference speed and efficiency
- Addressing scalability and deployment best practices
Conclusion and Future Directions
Requirements
- A solid grasp of core machine learning principles
- Practical experience with deep learning frameworks like PyTorch or TensorFlow
- Knowledge in natural language processing and computer vision
Target Audience
- Machine Learning Engineers
- AI Researchers
- Product Developers integrating visual and textual workflows
