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Duration 21 hours
Course Outline
Introduction to Multimodal AI and Ollama
- An overview of multimodal learning concepts.
- Key challenges associated with vision-language integration.
- The capabilities and underlying architecture of Ollama.
Setting Up the Ollama Environment
- Installation and configuration procedures for Ollama.
- Managing local model deployment workflows.
- Integrating Ollama with Python and Jupyter environments.
Handling Multimodal Inputs
- Integrating text and image data streams.
- Incorporating audio signals and structured data types.
- Designing effective preprocessing pipelines.
Document Understanding Applications
- Extracting structured information from PDFs and images.
- Combining OCR technologies with language models.
- Creating intelligent workflows for document analysis.
Visual Question Answering (VQA)
- Preparing VQA datasets and relevant benchmarks.
- Training and evaluating multimodal models.
- Developing interactive VQA applications.
Designing Multimodal Agents
- Core principles of agent design with multimodal reasoning.
- Synthesizing perception, language processing, and action.
- Deploying agents for practical real-world use cases.
Advanced Integration and Optimization
- Fine-tuning multimodal models using Ollama.
- Strategies for optimizing inference performance.
- Considerations for scalability and production deployment.
Summary and Future Directions
Requirements
- A solid grasp of core machine learning concepts.
- Practical experience with deep learning frameworks such as PyTorch or TensorFlow.
- Knowledge of natural language processing and computer vision principles.
Target Audience
- Machine learning engineers.
- AI researchers.
- Product developers integrating vision and text workflows.
