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Duration 21 hours
Course Outline
Fundamentals of Multimodal AI and Ollama
- Introduction to multimodal learning paradigms
- Core challenges in integrating vision and language
- Exploring the capabilities and architecture of Ollama
Preparing the Ollama Environment
- Installation and configuration of Ollama
- Managing local model deployment strategies
- Connecting Ollama with Python and Jupyter notebooks
Handling Multimodal Inputs
- Integrating text and image data
- Including audio streams and structured data types
- Architecting effective preprocessing pipelines
Applications in Document Understanding
- Pulling structured insights from PDFs and images
- Merging OCR technology with language models
- Constructing intelligent workflows for document analysis
Visual Question Answering (VQA)
- Establishing VQA datasets and performance benchmarks
- Training and assessing multimodal models
- Developing interactive VQA-based applications
Architecting Multimodal Agents
- Core principles of agent design with multimodal reasoning
- Synthesizing perception, language, and action
- Deploying agents for practical use cases
Advanced Integration and Performance Tuning
- Fine-tuning multimodal models using Ollama
- Enhancing inference speed and efficiency
- Addressing scalability and deployment challenges
Recap and Future Directions
Requirements
- A solid grasp of core machine learning concepts
- Proficiency 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 incorporating vision and text-based workflows
