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Duration 21 hours
Course Outline
Introduction to Multimodal AI and Ollama
- An overview of multimodal learning principles
- Key challenges in integrating vision and language
- Understanding Ollama's capabilities and architecture
Setting Up the Ollama Environment
- Installing and configuring the Ollama environment
- Managing local model deployment
- Connecting Ollama with Python and Jupyter notebooks
Handling Multimodal Inputs
- Integrating text and image data
- Incorporating audio and structured data formats
- Designing effective preprocessing pipelines
Document Understanding Applications
- Extracting structured insights from PDFs and images
- Enhancing language models with OCR technology
- Creating intelligent workflows for document analysis
Visual Question Answering (VQA)
- Preparing VQA datasets and evaluation benchmarks
- Training and assessing multimodal models
- Developing interactive VQA applications
Designing Multimodal Agents
- Core principles of agent design with multimodal reasoning
- Synthesising perception, language, and action
- Deploying agents for practical real-world scenarios
Advanced Integration and Optimisation
- Fine-tuning multimodal models using Ollama
- Optimising inference performance
- Considerations for scalability and deployment
Summary and Next Steps
Requirements
- A solid grasp of core machine learning concepts
- Hands-on experience with deep learning frameworks like 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
