Course Outline
Introduction to Generative Artificial Intelligence
- Definition and strategic significance of generative AI in public sector contexts.
- Primary categories and methodological approaches within generative AI.
- Critical challenges and operational limitations associated with generative AI systems.
Transformer Architecture and Large Language Models (LLMs)
- Fundamentals of transformer architecture and its operational mechanisms.
- Core components and structural attributes of transformer models.
- Development of LLMs utilizing transformer-based frameworks.
Scaling Principles and Optimization Strategies
- Understanding scaling laws and their relevance to LLM performance for government applications.
- Correlation between scaling laws and parameters including model size, dataset volume, computational resources, and inference demands.
- Utilizing scaling laws to enhance the efficacy and resource efficiency of LLMs.
Training and Fine-Tuning Large Language Models
- Key procedural steps and technical challenges involved in training LLMs from the ground up.
- Assessment of benefits and limitations associated with fine-tuning LLMs for specialized functions.
- Recommended methodologies and technical tools for effective training and fine-tuning processes.
Deployment and Operational Use of LLMs
- Critical factors and challenges in deploying LLMs within production environments for government operations.
- Representative use cases and applications of LLMs across various public sector domains.
- Integration of LLMs with existing AI ecosystems and institutional platforms.
Ethics, Accountability, and the Future of Generative AI
- Ethical considerations and societal impacts of generative AI and LLMs in the public interest.
- Potential risks, including bias, misinformation, and manipulation, and their mitigation strategies.
- Frameworks for the responsible and beneficial deployment of generative AI technologies.
Conclusions and Recommended Next Steps
Requirements
- Foundational comprehension of machine learning principles, including supervised and unsupervised learning paradigms, loss function mechanics, and data partitioning strategies.
- Proficiency in Python programming and data manipulation techniques.
- Fundamental knowledge of neural network structures and natural language processing concepts.
Target Audience
- Software developers and technical engineers.
- Professionals engaged in machine learning research and application.
Testimonials (7)
Examples and links excel repository
Olga - GE HealthCare
Course - Generative AI with Large Language Models (LLMs)
a lot of examples and different tools to check
Bartosz - GE HealthCare
Course - Generative AI with Large Language Models (LLMs)
Custom GPTs, prompt engineering
Marcin Stezowski - GE HealthCare
Course - Generative AI with Large Language Models (LLMs)
Wide perspective
Artur - GE HealthCare
Course - Generative AI with Large Language Models (LLMs)
Technical examples in conjunction with theory.
Marcin - GE HealthCare
Course - Generative AI with Large Language Models (LLMs)
Mikołaj background outside IT enable presenting this topic from different angle - much needed for IT folks!
Grzegorz - GE HealthCare
Course - Generative AI with Large Language Models (LLMs)
Explanation form other than IT perspective. Adding value
