Course Outline
Introduction to Generative AI for Government
- An overview of generative AI and its significance in the public sector.
- Principal types and techniques employed in generative AI.
- Key challenges and limitations associated with generative AI implementation for government.
Transformer Architecture and LLMs
- Definition of a transformer model and its operational mechanics.
- Core components and features that define a transformer architecture.
- Utilizing transformers to develop large language models (LLMs).
Scaling Laws and Optimization
- Explanation of scaling laws and their importance for the performance of LLMs.
- The relationship between scaling laws, model size, data volume, computational resources, and inference requirements.
- Strategies to leverage scaling laws for optimizing the efficiency and effectiveness of LLMs.
Training and Fine-Tuning LLMs
- Key steps and challenges involved in training LLMs from scratch.
- Advantages and disadvantages of fine-tuning LLMs for specific tasks within government workflows.
- Best practices and tools recommended for the training and fine-tuning of LLMs.
Deploying and Using LLMs
- Essential considerations and challenges in deploying LLMs in production environments for government.
- Common applications and use cases of LLMs across various domains and industries, including public sector scenarios.
- Methods for integrating LLMs with other AI systems and platforms to enhance government operations.
Ethics and Future of Generative AI
- Ethical and social implications of generative AI and LLMs in the context of public service.
- Potential risks and harms associated with generative AI and LLMs, such as bias, misinformation, and manipulation.
- Strategies for responsible and beneficial deployment of generative AI and LLMs to support government objectives.
Summary and Next Steps
Requirements
- A comprehensive understanding of machine learning principles, including supervised and unsupervised learning, loss functions, and data partitioning techniques.
- Practical experience with Python programming and data manipulation for government applications.
- Fundamental knowledge of neural networks and natural language processing methodologies.
Audience
- Software developers
- Machine learning professionals for government
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
