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Duration 21 hours
Course Outline
Foundations of Multimodal AI and Ollama
- An introduction to multimodal learning concepts
- Addressing key challenges in integrating vision and language
- Exploring the capabilities and architecture of Ollama
Establishing the Ollama Workspace
- Installation and configuration procedures for Ollama
- Strategies for local model deployment
- Connecting Ollama with Python and Jupyter environments
Handling Multimodal Data Inputs
- Combining text and image data streams
- Incorporating audio and structured data formats
- Architecting effective preprocessing pipelines
Applications in Document Comprehension
- Pulling structured information from PDFs and visual assets
- Synergizing OCR technology with language models
- Constructing intelligent workflows for document analysis
Visual Question Answering (VQA)
- Configuring VQA datasets and performance benchmarks
- Training and assessing multimodal models
- Developing interactive VQA applications
Architecting Multimodal Agents
- Core principles of agent design utilizing multimodal reasoning
- Fusing perception, language processing, and action
- Deploying agents for practical, real-world scenarios
Advanced Integration and Performance Tuning
- Refining multimodal models through fine-tuning with Ollama
- Enhancing inference speed and efficiency
- Addressing scalability and deployment requirements
Conclusion and Future Pathways
Requirements
- A solid grasp of core machine learning principles
- Hands-on experience with deep learning frameworks like PyTorch or TensorFlow
- Proficiency in natural language processing and computer vision
Target Audience
- Machine learning engineers
- AI researchers
- Product developers integrating vision and text workflows
