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Duration 21 hours
Course Outline
Intro to Multimodal AI and Ollama
- Foundations of multimodal learning
- Primary obstacles in integrating vision and language
- Ollama’s features and architectural design
Configuring the Ollama Environment
- Installation and setup of Ollama
- Managing local model deployment
- Connecting Ollama with Python and Jupyter Notebooks
Handling Multimodal Data Inputs
- Merging text and image data
- Including audio and structured information
- Creating effective preprocessing pipelines
Applications in Document Comprehension
- Pulling structured data from PDFs and images
- Pairing OCR technology with language models
- Constructing smart document analysis workflows
Visual Question Answering (VQA)
- Preparing VQA datasets and benchmarks
- Training and assessing multimodal models
- Creating interactive VQA interfaces
Architecting Multimodal Agents
- Core principles of agent design with multimodal reasoning
- Synthesizing perception, language, and actions
- Deploying agents for practical scenarios
Advanced Integration and Performance Tuning
- Fine-tuning multimodal models using Ollama
- Enhancing inference speed and efficiency
- Considerations for scaling and deployment
Conclusion and Future Directions
Requirements
- Solid grasp of core machine learning principles
- Hands-on experience with deep learning frameworks like PyTorch or TensorFlow
- Knowledge of natural language processing and computer vision techniques
Intended Audience
- Machine learning engineers
- AI researchers
- Product developers working with vision and text integration
