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Duration 21 hours
Course Outline
Introduction to Multimodal AI and Ollama
- Overview of multimodal learning paradigms
- Key challenges in integrating vision and language models
- Exploring Ollama's capabilities and underlying architecture
Setting Up the Ollama Environment
- Installation and configuration of Ollama
- Managing local model deployment strategies
- Connecting Ollama with Python and Jupyter Notebooks
Handling Multimodal Inputs
- Integrating text and image data streams
- Incorporating audio and structured data formats
- Architecting efficient preprocessing pipelines
Document Understanding Applications
- Extracting structured information from PDFs and visual content
- Merging OCR capabilities with large language models
- Constructing intelligent workflows for document analysis
Visual Question Answering (VQA)
- Establishing VQA datasets and performance benchmarks
- Training and assessing multimodal model performance
- Developing interactive VQA application interfaces
Designing Multimodal Agents
- Core principles of agent design featuring multimodal reasoning
- Harmonizing perception, language processing, and action execution
- Deploying autonomous agents for practical use cases
Advanced Integration and Optimization
- Fine-tuning multimodal models specifically within Ollama
- Enhancing inference speed and efficiency
- Addressing scalability and production deployment challenges
Summary and Next Steps
Requirements
- A solid grasp of fundamental machine learning principles
- Hands-on experience with deep learning frameworks like PyTorch or TensorFlow
- Knowledge of natural language processing and computer vision techniques
Target Audience
- Machine learning engineers
- AI researchers
- Product developers working on integrating vision and text workflows
