Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Duration 21 hours
Course Outline
Foundations of Multimodal AI and Ollama
- An introduction to multimodal learning concepts
- Primary challenges in integrating vision and language
- Understanding Ollama's capabilities and architecture
Preparing the Ollama Development Environment
- Installation and configuration of Ollama
- Managing local model deployments
- Connecting Ollama with Python and Jupyter notebooks
Handling Multimodal Inputs
- Merging text and image data streams
- Incorporating audio and structured data formats
- Architecting efficient preprocessing pipelines
Applications in Document Understanding
- Extracting structured insights from PDFs and images
- Augmenting language models with OCR technology
- Constructing intelligent workflows for document analysis
Visual Question Answering (VQA)
- Establishing VQA datasets and performance benchmarks
- Training and assessing multimodal models
- Creating interactive VQA solutions
Architecting Multimodal Agents
- Core principles of agent design using multimodal reasoning
- Synthesizing perception, language processing, and action execution
- Implementing agents for practical use cases
Advanced Integration and Performance Tuning
- Refining multimodal models via Ollama
- Enhancing inference speed and efficiency
- Addressing scalability and deployment strategies
Recap and Future Directions
Requirements
- A solid grasp of core machine learning principles
- Hands-on 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 workflows
