Course Outline
Foundations of Generative AI
- Defining generative AI and understanding its strategic importance.
- Exploring the primary categories and core techniques of generative AI.
- Identifying key challenges and current limitations within the field.
Transformer Architecture and LLM Mechanics
- Understanding the transformer model and its functional principles.
- Examining the essential components and characteristics of a transformer.
- Leveraging transformer architectures to construct robust LLMs.
Scaling Laws and Performance Optimization
- Understanding scaling laws and their critical role in LLM development.
- Analyzing the relationship between scaling laws and model size, data volume, compute resources, and inference needs.
- Utilizing scaling laws to enhance LLM efficiency and performance.
Training and Fine-Tuning Strategies for LLMs
- Navigating the key steps and complexities of training LLMs from scratch.
- Weighing the advantages and disadvantages of fine-tuning LLMs for specialized tasks.
- Adopting best practices and utilizing effective tools for training and fine-tuning processes.
LLM Deployment and Practical Application
- Addressing critical considerations and challenges when deploying LLMs in production environments.
- Reviewing common use cases and applications of LLMs across various industries.
- Integrating LLMs seamlessly with other AI systems and enterprise platforms.
Ethics and the Future Landscape of Generative AI
- Assessing the ethical and societal impacts of generative AI and LLMs.
- Identifying potential risks and harms, such as bias, misinformation, and manipulative tactics.
- Promoting the responsible and beneficial use of generative AI technologies.
Course Summary and Future Directions
Requirements
- A solid grasp of machine learning fundamentals, including supervised and unsupervised learning, loss functions, and data partitioning techniques.
- Proficiency in Python programming and data handling.
- Foundational knowledge of neural networks and natural language processing (NLP).
Target Audience
- Software Developers
- Machine Learning Enthusiasts
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
