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Duration 21 hours
Course Outline
Foundations of Multimodal AI and Ollama
- Understanding the scope of multimodal learning.
- Addressing key challenges in vision-language integration.
- Exploring the capabilities and architecture of Ollama.
Configuring the Ollama Environment
- Installation and setup of Ollama.
- Managing local model deployment.
- Connecting Ollama with Python and Jupyter environments.
Handling Multimodal Inputs
- Combining text and image data.
- Including audio and structured data types.
- Designing effective preprocessing pipelines.
Applications in Document Understanding
- Extracting structured insights from PDFs and images.
- Enhancing language models with OCR technology.
- Creating intelligent workflows for document analysis.
Visual Question Answering (VQA)
- Preparing VQA datasets and benchmarks.
- Training and assessing multimodal models.
- Developing interactive VQA solutions.
Architecting Multimodal Agents
- Core principles of agent design involving multimodal reasoning.
- Synthesizing perception, language, and action.
- Rolling out agents for practical use cases.
Advanced Integration and Performance Tuning
- Fine-tuning multimodal models using Ollama.
- Enhancing inference speed and efficiency.
- Considerations for scalability and production deployment.
Conclusion and Future Directions
Requirements
- A solid grasp of core machine learning concepts.
- Proficiency with deep learning frameworks like PyTorch or TensorFlow.
- Working knowledge of natural language processing and computer vision.
Target Audience
- Machine learning engineers.
- AI researchers.
- Product developers integrating vision and text-based workflows.
