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Duration 21 hours
Course Outline
Foundations of Multimodal AI and Ollama
- Overview of multimodal learning paradigms
- Primary challenges in integrating vision and language
- Architectural features and capabilities of Ollama
Preparing the Ollama Environment
- Installation and configuration of Ollama
- Managing local model deployment
- Connecting Ollama with Python and Jupyter environments
Handling Multimodal Inputs
- Merging text and image data streams
- Including audio and structured data formats
- Structuring data preprocessing workflows
Applications in Document Comprehension
- Extracting structured insights from PDFs and images
- Fusing OCR technology with language models
- Creating automated document analysis workflows
Visual Question Answering (VQA)
- Establishing VQA datasets and performance benchmarks
- Training and assessing multimodal models
- Developing interactive VQA solutions
Architecting Multimodal Agents
- Core principles of agent design with multimodal reasoning
- Unifying perception, language, and action components
- Implementing agents for practical real-world scenarios
Advanced Integration and Performance Optimization
- Fine-tuning multimodal models using Ollama
- Enhancing inference speed and efficiency
- Considerations for scalability and deployment
Conclusions and Future Directions
Requirements
- Solid grasp of fundamental machine learning concepts
- Practical 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 vision and text integration workflows
