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Reinforcement Learning from Human Feedback (RLHF)

by Jerry Kurata

In this course we explore one corner of the expanding AI universe, and review some of the basic principles found in reinforcement learning from human feedback (RLHF), the technology underlying great AI tools such as ChatGPT, Bard, and more.

What you'll learn

Have you ever wondered how tools like ChatGPT and Bard are able to generate great responses to the questions we pose? How they can respond to a prompt like “Plan a trip to Italy this fall and suggest great things to see,” and produce a response containing a full itinerary with places to see, the best time to visit, and the sites you shouldn't miss?

In this course, Reinforcement Learning from Human Feedback (RLHF), you’ll gain the ability to understand what is going on behind the scenes to create responses to your prompts.

First, you’ll explore why having all the information available is not enough to create a great response.

Next, you’ll discover how we teach a machine learning model to handle all that data and craft a response that people like.

Finally, you’ll learn how none of it is magic, just some really great engineering by some bright people.

When you’re finished with this course, you’ll have the skills and knowledge of reinforcement learning with human feedback needed to understand how this great engineering works and produces its amazing results.

Table of contents

Course Overview
Understanding Text-generative Applications
What Is Wrong with the Pre-trained GPT Model?
Supervised Fine-tuning
Fine-tuning via Reinforcement Learning
Implementing RLHF
Challenges and Limitations of RLHF

About the author

Jerry has Bachelor of Science degrees in Geology and Physics. His plans to work in the oil exploration industry were sidetracked when he discovered he preferred to work with computers on simulation and data processing, instead of reading mud and core samples in the North Sea. His love of computers and tech resulted in him spending many additional hours working on computers while getting his Master’s degree in Computer Science. His current areas of interests include Machine Learning, Big Data,... more

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