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Course Outline
Introduction to Multimodal AI and Ollama
- Overview of multimodal learning.
- Key challenges in vision-language integration.
- Capabilities and architecture of Ollama.
Setting Up the Ollama Environment
- Installing and configuring Ollama.
- Working with local model deployment.
- Integrating Ollama with Python and Jupyter.
Working with Multimodal Inputs
- Text and image integration.
- Incorporating audio and structured data.
- Designing preprocessing pipelines.
Document Understanding Applications
- Extracting structured information from PDFs and images.
- Combining OCR with language models.
- Building intelligent document analysis workflows.
Visual Question Answering (VQA)
- Setting up VQA datasets and benchmarks.
- Training and evaluating multimodal models.
- Building interactive VQA applications.
Designing Multimodal Agents
- Principles of agent design with multimodal reasoning.
- Combining perception, language, and action.
- Deploying agents for real-world use cases.
Advanced Integration and Optimization
- Fine-tuning multimodal models with Ollama.
- Optimizing inference performance.
- Scalability and deployment considerations.
Summary and Next Steps
Requirements
- Strong grasp of machine learning concepts.
- Experience with deep learning frameworks such as PyTorch or TensorFlow.
- Familiarity with natural language processing and computer vision techniques.
Audience
- Machine learning engineers.
- AI researchers.
- Product developers integrating vision and text workflows.
21 Hours
Custom Corporate Training
Training solutions designed exclusively for businesses.
- Customized Content: We adapt the syllabus and practical exercises to the real goals and needs of your project.
- Flexible Schedule: Dates and times adapted to your team's agenda.
- Format: Online (live), In-company (at your offices), or Hybrid.
Price per private group, online live training, starting from 4800 € + VAT*
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