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Duration 21 hours
Course Outline
Introduction to Multimodal AI and Ollama
- Fundamentals of multimodal learning
- Key challenges in integrating vision and language models
- Understanding Ollama's capabilities and architectural design
Establishing the Ollama Environment
- Installation and configuration of Ollama
- Strategies for local model deployment
- Integrating Ollama with Python and Jupyter notebooks
Handling Multimodal Inputs
- Effective integration of text and image data
- Incorporating audio streams and structured data sources
- Architecting efficient preprocessing pipelines
Document Understanding Solutions
- Extracting structured insights from PDFs and images
- Leveraging OCR in conjunction with language models
- Creating intelligent workflows for document analysis
Visual Question Answering (VQA)
- Preparing VQA datasets and establishing benchmarks
- Training and evaluating multimodal models for accuracy
- Developing interactive VQA applications
Developing Multimodal Agents
- Core principles of agent design with multimodal reasoning
- Synthesizing perception, language, and action capabilities
- Deploying agents for practical, real-world use cases
Advanced Integration and Performance Tuning
- Fine-tuning multimodal models specifically with Ollama
- Optimizing inference speed and performance
- Addressing scalability and production deployment considerations
Wrap-up and Future Directions
Requirements
- A solid grasp of core machine learning principles
- Proficiency with deep learning frameworks such as PyTorch or TensorFlow
- Working knowledge of natural language processing (NLP) and computer vision
Target Audience
- Machine learning engineers
- AI researchers
- Product developers integrating vision and text-based workflows
