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Course Outline
Introduction to Multimodal AI and Ollama
- Comprehensive overview of multimodal learning
- Key challenges associated with vision-language integration
- Capabilities and architectural design of Ollama
Setting Up the Ollama Environment
- Installation and configuration of Ollama
- Managing local model deployment
- Integrating Ollama with Python and Jupyter notebooks
Working with Multimodal Inputs
- Integration of text and image data
- Incorporating audio streams and structured data
- Designing effective preprocessing pipelines
Document Understanding Applications
- Extracting structured information from PDFs and images
- Combining optical character recognition with language models
- Constructing intelligent workflows for document analysis
Visual Question Answering (VQA)
- Establishing VQA datasets and benchmarking standards
- Training and evaluating multimodal models
- Developing interactive VQA applications
Designing Multimodal Agents
- Core principles of agent design involving multimodal reasoning
- Merging perception, language, and action capabilities
- Deploying agents for real-world use cases
Advanced Integration and Optimization
- Fine-tuning multimodal models using Ollama
- Optimizing inference performance
- Addressing scalability and deployment considerations
Summary and Next Steps
Requirements
- A solid grasp of fundamental machine learning principles
- Practical experience with deep learning frameworks such as PyTorch or TensorFlow
- Familiarity with natural language processing and computer vision techniques
Target Audience
- Machine learning engineers
- Artificial intelligence researchers
- Product developers who integrate visual and textual workflows
21 Hours
