Curso
Reinforcement Learning from Human Feedback (RLHF)
AvanzadoNivel de habilidad
Actualizado 10/2024Comienza El Curso Gratis
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PythonArtificial Intelligence4 h13 vídeos38 Ejercicios2,900 XP3,342Certificado de logros
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Requisitos previos
Deep Reinforcement Learning in Python1
Foundational Concepts
This chapter introduces the basics of Reinforcement Learning with Human Feedback (RLHF), a technique that uses human input to help AI models learn more effectively. Get started with RLHF by understanding how it differs from traditional reinforcement learning and why human feedback can enhance AI performance in various domains.
2
Gathering Human Feedback
Discover how to set up systems for gathering human feedback in this Chapter. Learn best practices for collecting high-quality data, from pairwise comparisons to uncertainty sampling, and explore strategies for enhancing your data collection.
3
Tuning Models with Human Feedback
In this Chapter, you'll get into the core of Reinforcement Learning from Human Feedback training. This includes exploring fine-tuning with PPO, techniques to train efficiently, and handling potential divergences from your metrics' objectives.
4
Model Evaluation
Explore key techniques for assessing and improving model performance in this last Chapter of Reinforcement Learning from Human Feedback (RLHF): from fine-tuning metrics to incorporating diverse feedback sources, you'll be provided with a comprehensive toolkit to refine your models effectively.
Reinforcement Learning from Human Feedback (RLHF)
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