Generative AI LLMs

Topics covered in the exam include:

• Fundamentals of machine learning and neural networks
• Prompt engineering
• Alignment
• Data analysis and visualization
• Experimentation
• Data Preprocessing and feature engineering
• Experiment design
• Software development
• Python libraries for LLMs
• LLM integration and deployment

We recommend the following activities to prepare for this exam.

• Generative AI Explained (self-paced course, 2 hours, free) 
• Getting Started With Deep Learning (self-paced course, 8 hours, $90)
  or
  Fundamentals of Deep Learning (instructor-led workshop, 8 hours, $500) 
• Fundamentals of Accelerated Data Science (instructor-led workshop, 8 hours, $500) 
• Introduction to Transformer-Based Natural Language Processing (self-paced course, 6 hours, $30) 
• Building Transformer-Based Natural Language Processing Applications (instructor-led workshop, 8 hours, $500) 
• Rapid Application Development Using LLMs (instructor-led workshop, 8 hours, $500) 
• Efficient Large Language Model (LLM) Customization (instructor-led workshop, 8 hours, $500) 
• Prompt Engineering With LLaMA-2 (self-paced course, 3 hours, $30) 
• Augmenting Your LLM Using Retrieval-Augmented Generation (self-paced course, 1 hour, free)
• Building RAG Agents With LLMs (self-paced course, 8 hours, free)
  or
  Building RAG Agents with LLMs (instructor-led workshop, 8 hours, $500)

• Getting Started With Large Language Models for Enterprise Solutions (blog, 20 minutes)
• The Fast Path to Developing With LLMs (on-demand video, 50 minutes)
• Running Your Own LLM (on-demand video, 40 minutes)
• What Is Retrieval-Augmented Generation, aka RAG? (blog, 15 minutes)
• Trustworthy AI (webpage)