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Duration 21 hours
Course Outline
Fundamentals of Multimodal AI and Ollama
- Overview of multimodal learning concepts
- Challenges in integrating vision and language models
- Exploring Ollama's architecture and capabilities
Establishing the Ollama Environment
- Installation and configuration of Ollama
- Managing local model deployments
- Connecting Ollama with Python and Jupyter notebooks
Handling Multimodal Data Inputs
- Merging text and image data streams
- Including audio and structured data formats
- Creating effective preprocessing workflows
Applications in Document Understanding
- Pulling structured data from PDFs and images
- Pairing OCR technology with language models
- Constructing smart document analysis processes
Visual Question Answering (VQA)
- Preparing VQA datasets and establishing benchmarks
- Training and assessing multimodal models
- Creating interactive VQA solutions
Architecting Multimodal Agents
- Core principles of agent design with multimodal reasoning
- Synthesizing perception, language, and action
- Deploying agents for practical business use cases
Advanced Integration and Performance Optimization
- Fine-tuning multimodal models using Ollama
- Enhancing inference speed and efficiency
- Addressing scalability and deployment strategies
Recap 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 implementing vision and text-based workflows
