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Duration 21 hours
Course Outline
Fundamentals of Multimodal AI and Ollama
- Brief on multimodal learning
- Primary hurdles in combining vision and language
- Ollama's features and system architecture
Configuring the Ollama Environment
- Installation and setup of Ollama
- Managing local model deployment
- Connecting Ollama to Python and Jupyter
Handling Multimodal Inputs
- Merging text and image data
- Including audio and structured datasets
- Creating preprocessing workflows
Applications for Document Comprehension
- Retrieving structured details from PDFs and images
- Pairing OCR with language models
- Developing smart document analysis processes
Visual Question Answering (VQA)
- Preparing VQA datasets and benchmarks
- Training and assessing multimodal models
- Creating interactive VQA tools
Architecting Multimodal Agents
- Core concepts of agent design with multimodal reasoning
- Synthesising perception, language, and action
- Implementing agents for practical applications
Sophisticated Integration and Optimisation
- Fine-tuning multimodal models via Ollama
- Enhancing inference speed
- Addressing scalability and deployment needs
Wrap-up and Future Directions
Requirements
- Profound grasp of machine learning principles
- Proficiency with deep learning frameworks like PyTorch or TensorFlow
- Knowledge of natural language processing and computer vision
Intended Audience
- Machine learning engineers
- AI researchers
- Product developers integrating vision and text workflows
