deep-learning-drizzle
awesome-RLHF
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| 1 | 6 | |
| 11,764 | 2,739 | |
| - | 4.3% | |
| 0.0 | 7.0 | |
| 3 months ago | 15 days ago | |
| HTML | ||
| - | Apache License 2.0 |
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deep-learning-drizzle
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Consolidated Video lectures for Machine Learning(including DL, CV, NLP, etc)
Also this as well for whoever needs it
awesome-RLHF
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OpenDILab Awesome Paper Collection: RL with Human Feedback （3）
Recently, OpenDILab made a paper collection about Reinforcement Learning with Human Feedback (RLHF) and it has been open-sourced on GitHub. This repository is dedicated to helping researchers to collect the latest papers on RLHF, so that they can get to know this area better and more easily.
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Awesome RLHF (RL with Human Feedback) This is a collection of research papers for Reinforcement Learning with Human Feedback (RLHF). And the repository will be continuously updated to track the frontier of RLHF. Welcome to follow and star! https://github.com/opendilab/awesome-RLHF
Welcome to follow and star! https://github.com/opendilab/awesome-RLHF
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OpenDILab Awesome Paper Collection: RL with Human Feedback （1）
Here we’re gonna introduce a new repository open-sourced by OpenDILab. Recently, OpenDILab made a paper collection about Reinforcement Learning with Human Feedback (RLHF) and it has been open-sourced on GitHub. This repository is dedicated to helping researchers to collect the latest papers on RLHF, so that they can get to know this area better and more easily. About RLHF Reinforcement Learning with Human Feedback (RLHF) is an extended branch of Reinforcement Learning (RL) that allows the RLHF family of methods to incorporate human feedback into the training process by using this feedback to construct By using this feedback to build a reward model neural network that provides reward signals to help RL intelligences learn, human needs, preferences, and perceptions can be more naturally communicated to the intelligence in an interactive learning manner, aligning the optimization goals between humans and artificial intelligence to produce systems that behave in a manner consistent with human values. Reinforcement Learning with Human Feedback (RLHF) is an extended branch of Reinforcement Learning. When the optimization goal is abstract and it's very difficult to define the specific reward function, RLHF can help to put human feedback into the training process. This feedback can be constructed into a reward neural network model so that RL agents can learn from the given reward signal and naturally convey human needs, preference and attitude to agents through interactive learning.
- A collection of research papers for Reinforcement Learning with Human Feedback (RLHF)
What are some alternatives?
cs229-solution - CS229 Solution (summer 2019, 2020).
Practical_RL - A course in reinforcement learning in the wild
OPUS-MT-train - Training open neural machine translation models
LaMDA-rlhf-pytorch - Open-source pre-training implementation of Google's LaMDA in PyTorch. Adding RLHF similar to ChatGPT.
ultimate-volleyball-starter - Tutorial kit for building a 3D deep reinforcement learning environment with Unity ML-Agents.
hh-rlhf - Human preference data for "Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback"
awesome-full-stack-machine-learning-courses - Curated list of publicly accessible machine learning engineering courses from CalTech, Columbia, Berkeley, MIT, and Stanford.
visual-chatgpt - Official repo for the paper: Visual ChatGPT: Talking, Drawing and Editing with Visual Foundation Models [Moved to: https://github.com/microsoft/TaskMatrix]
bidd-molmap - MolMapNet: An Efficient ConvNet with Knowledge-based Molecular Represenations for Molecular Deep Learning
deep-rl-class - This repo contains the syllabus of the Hugging Face Deep Reinforcement Learning Course.
Contour-Based-Writing - This is a simple concept to do writing like operation using the contours. Please follow the article https://q-viper.github.io/2020/08/28/gesture-based-visually-writing-system-web-app/ for further details.
ChessVision - Extract chess positions from photos of 2D chessboards (chess books, screenshots, etc.)