Reinforcement learning from human feedback (RLHF) is a technique that trains AI models by learning from responses provided by humans. It uses a reward model, trained in advance, to optimize the agent's policy through reinforcement learning. RLHF has been successfully applied in natural language processing tasks and video game bot development, achieving strong performance and surpassing human capabilities in some cases.
Stanford University
Winter 2023
This course provides a deeper understanding of robot autonomy principles, focusing on learning new skills and physical interaction with the environment and humans. It requires familiarity with programming, ROS, and basic robot autonomy techniques.
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+ 13 more conceptsStanford University
Winter 2023
CS 224N provides an in-depth introduction to neural networks for NLP, focusing on end-to-end neural models. The course covers topics such as word vectors, recurrent neural networks, and transformer models, among others.
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+ 21 more conceptsStanford University
Winter 2023
This comprehensive course covers various machine learning principles from supervised, unsupervised to reinforcement learning. Topics also touch on neural networks, support vector machines, bias-variance tradeoffs, and many real-world applications. It requires a background in computer science, probability, multivariable calculus, and linear algebra.
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