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ORLM: Training Large Language Models for Optimization Modeling
Minimal implementation of clipped objective Proximal Policy Optimization (PPO) in PyTorch
Massively Parallel Deep Reinforcement Learning. 🔥
📚LeetCUDA: Modern CUDA Learn Notes with PyTorch for Beginners🐑, 200+ CUDA/Tensor Cores Kernels, HGEMM, FA-2 MMA etc.🔥
Clean, Robust, and Unified PyTorch implementation of popular Deep Reinforcement Learning (DRL) algorithms (Q-learning, Duel DDQN, PER, C51, Noisy DQN, PPO, DDPG, TD3, SAC, ASL)
Train your grpo with zero dataset and low resources, 8bit/4bit/lora/qlora supported, multi-gpu supported ...
A collection of research and survey papers of real-time bidding (RTB) based display advertising techniques.
计算广告机制策略相关材料整理(A collection of research and application papers about Strategy in Internet advertising.)
Companion webpage to the book "Mathematics For Machine Learning"
PyTorch implementation of DQN, AC, ACER, A2C, A3C, PG, DDPG, TRPO, PPO, SAC, TD3 and ....
codes for SORL framework for auto-bidding
AuctionNet: A Novel Benchmark for Decision-Making in Large-Scale Games
强化学习中文教程（蘑菇书🍄），在线阅读地址：https://datawhalechina.github.io/easy-rl/
GitHub's code repository is all you need
搜索、推荐、广告、用增等工业界实践文章收集（来源：知乎、Datafuntalk、技术公众号）
Budget Constrained Bidding for Display Advertising using Model-free Reinforcement Learning
本项目旨在分享大模型相关技术原理以及实战经验（大模型工程化、大模型应用落地）
精选机器学习，NLP，图像识别， 深度学习等人工智能领域学习资料，搜索，推荐，广告系统架构及算法技术资料整理。算法大牛笔记汇总
《李宏毅深度学习教程》（李宏毅老师推荐👍，苹果书🍎），PDF下载地址：https://github.com/datawhalechina/leedl-tutorial/releases
Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.
Repo for the Deep Reinforcement Learning Nanodegree program
Pytorch🍊🍉 is delicious, just eat it! 😋😋
Understanding Deep Learning - Simon J.D. Prince
Must-read papers and resources related to causal inference and machine (deep) learning
Extensive tutorials for learning how to build deep learning models for causal inference (HTE) using selection on observables in Tensorflow 2 and Pytorch.
An index of algorithms for learning causality with data
🔥Highlighting the top ML papers every week.