Reinforcement Learning from Human Feedback (RLHF)

In this course, you will develop the skills to train and fine-tune AI models using Reinforcement Learning with Human Feedback (RLHF). You'll learn to differentiate RLHF from traditional reinforcement learning, fine-tune pre-trained large language models (LLMs), gather and process human feedback, and use advanced techniques like Proximal Policy Optimization (PPO) and LoRA for efficient fine-tuning. You'll also gain the expertise to evaluate and analyze feedback quality for real-world AI applications.

This course is ideal for machine learning engineers, AI researchers, and AI practitioners who want to enhance their skills in RLHF and model fine-tuning. It will be especially beneficial if you already have a background in Python and experience with Hugging Face libraries such as transformers. It's also a good fit for professionals who train AI models and want to get started using human feedback to align their models' output with human preferences.

Yes! Every lesson includes hands-on exercises where you will apply what you've learned to real-world scenarios. You'll work with pre-trained models, fine-tune them using human feedback, and train reward models with techniques like Proximal Policy Optimization (PPO). These exercises will allow you to solidify your understanding of the concepts learned, while building practical skills that you can apply directly to your projects.

You'll have a variety of resources available throughout the course, such as detailed lecture slides, code examples, and interactive coding exercises. For additional practice, you can explore DataLab, where you can test your code in a fully cloud-based development environment.