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Duration 21 hours
Course Outline
Introduction to Multimodal AI and Ollama
- Fundamentals of multimodal learning
- Primary challenges in integrating vision and language
- Exploring the capabilities and architecture of Ollama
Configuring the Ollama Environment
- Installation and setup procedures for Ollama
- Managing local model deployments
- Connecting Ollama with Python and Jupyter environments
Handling Multimodal Inputs
- Merging text and image data
- Including audio and structured data formats
- Architecting preprocessing pipelines
Applications in Document Understanding
- Pulling structured data from PDFs and images
- Pairing OCR tools with language models
- Creating intelligent workflows for document analysis
Visual Question Answering (VQA)
- Preparing VQA datasets and benchmarking tools
- Training and assessing multimodal models
- Developing interactive VQA interfaces
Engineering Multimodal Agents
- Core principles of agent design involving multimodal reasoning
- Synthesizing perception, language, and action
- Implementing agents for practical use cases
Advanced Integration and Performance Tuning
- Fine-tuning multimodal models via Ollama
- Enhancing inference speed and efficiency
- Addressing scalability and deployment strategies
Conclusion and Future Directions
Requirements
- A solid grasp of core machine learning principles
- 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 integrating vision and text workflows
