AZDSG

AZDSG's Stars

• imarvinle/awesome-cs-books

  🔥 经典编程书籍大全，涵盖：计算机系统与网络、系统架构、算法与数据结构、前端开发、后端开发、移动开发、数据库、测试、项目与团队、程序员职业修炼、求职面试等

  17.5k244162.5k

• china-testing/python-api-tesing

  python测试开发库 中文版(持续更新)及书籍下载。公众号：pythontesting

  Language:Python2.4k1404879

• Tencent/GameAISDK

  基于图像的游戏AI自动化框架

  Language:C++2.1k4340435

• ML4Comm-Netw/Paper-with-Code-of-Wireless-communication-Based-on-DL

  无线与深度学习结合的论文代码整理/Paper-with-Code-of-Wireless-communication-Based-on-DL

  1.8k33922641

• GitDzreal93/dev-tester

  测试开发面试资源、复习资料汇总

  Language:HTML740103168

• swordest/mec_drl

  Deep reinforcement learning for mobile edge computing

  Language:Jupyter Notebook356126126

• GitDzreal93/awesome-tester

  测试开发各种资源汇总

  3165170

• IBM/adaptive-federated-learning

  Code for paper "Adaptive Federated Learning in Resource Constrained Edge Computing Systems"

  Language:Python2407781

• shkrwnd/Deep-Reinforcement-Learning-for-Dynamic-Spectrum-Access

  Using multi-agent Deep Q Learning with LSTM cells (DRQN) to train multiple users in cognitive radio to learn to share scarce resource (channels) equally without communication

  Language:Jupyter Notebook22312397

• jimkon/Deep-Reinforcement-Learning-in-Large-Discrete-Action-Spaces

  Implementation of the algorithm in Python 3, TensorFlow and OpenAI Gym

  Language:Python1729754

• haoyye/ResourceAllocationReinforcementLearning

  intial version

  Language:Python14611466

• atavakol/action-branching-agents

  (AAAI 2018) Action Branching Architectures for Deep Reinforcement Learning

  Language:Python10910823

• LuminLiu/HierFL

  Implementation of paper "Client-Edge-Cloud Hierarchical Federated Learning

  Language:Python1023930

• 011235813/hierarchical-marl

  Hierarchical Cooperative Multi-Agent Reinforcement Learning with Skill Discovery

  Language:Python913121

• FabianEckermann/ns-3_c-v2x

  Cellular Vehicle-to-Everything (C-V2X) Mode 4 model for ns-3

  Language:C++8161238

• ChangyWen/wolpertinger_ddpg

  Wolpertinger Training with DDPG (Pytorch), Deep Reinforcement Learning in Large Discrete Action Spaces. Multi-GPU/Singer-GPU/CPU compatible.

  Language:Python632616

• mablhq/github-run-tests-action

  mabl Github Actions implementation

  Language:TypeScript5427824

• MoMe36/BranchingDQN

  BranchingDQN

  Language:Python48335

• CooperLWang/Learn-CompressCSI-RA-V2X-Code

  Code for Learn to Compress CSI and Allocate Resources in Vehicular Networks

  Language:Python382316

• henkwymeersch/DeepRLVehicularLocalization

  Decentralized Scheduling for Cooperative Localization with Deep Reinforcement Learning

  Language:Python320222

• mengxiaomao/PA_ICC

  Language:Python323313

• wn-upf/decentralized_qlearning_resource_allocation_in_wns

  Language:TeX305028

• CrQiu/Energy-Harvesting-DDPG-

  Codes for "Deep Deterministic Policy Gradient (DDPG) based Energy Harvesting Wireless Communications"

  Language:Jupyter Notebook220212

• mathchi/Customer-Segmentation-with-RFM-Analysis

  Context A real online retail transaction data set of two years. Content This Online Retail II data set contains all the transactions occurring for a UK-based and registered, non-store online retail between 01/12/2009 and 09/12/2011.The company mainly sells unique all-occasion gift-ware. Many customers of the company are wholesalers. Column Descriptors InvoiceNo: Invoice number. Nominal. A 6-digit integral number uniquely assigned to each transaction. If this code starts with the letter 'c', it indicates a cancellation. StockCode: Product (item) code. Nominal. A 5-digit integral number uniquely assigned to each distinct product. Description: Product (item) name. Nominal. Quantity: The quantities of each product (item) per transaction. Numeric. InvoiceDate: Invice date and time. Numeric. The day and time when a transaction was generated. UnitPrice: Unit price. Numeric. Product price per unit in sterling (£). CustomerID: Customer number. Nominal. A 5-digit integral number uniquely assigned to each customer. Country: Country name. Nominal. The name of the country where a customer resides. Acknowledgements Here you can find references about data set: https://archive.ics.uci.edu/ml/datasets/Online+Retail+II and Relevant Papers: Chen, D. Sain, S.L., and Guo, K. (2012), Data mining for the online retail industry: A case study of RFM model-based customer segmentation using data mining, Journal of Database Marketing and Customer Strategy Management, Vol. 19, No. 3, pp. 197-208. doi: [Web Link]. Chen, D., Guo, K. and Ubakanma, G. (2015), Predicting customer profitability over time based on RFM time series, International Journal of Business Forecasting and Marketing Intelligence, Vol. 2, No. 1, pp.1-18. doi: [Web Link]. Chen, D., Guo, K., and Li, Bo (2019), Predicting Customer Profitability Dynamically over Time: An Experimental Comparative Study, 24th Iberoamerican Congress on Pattern Recognition (CIARP 2019), Havana, Cuba, 28-31 Oct, 2019. Laha Ale, Ning Zhang, Huici Wu, Dajiang Chen, and Tao Han, Online Proactive Caching in Mobile Edge Computing Using Bidirectional Deep Recurrent Neural Network, IEEE Internet of Things Journal, Vol. 6, Issue 3, pp. 5520-5530, 2019. Rina Singh, Jeffrey A. Graves, Douglas A. Talbert, William Eberle, Prefix and Suffix Sequential Pattern Mining, Industrial Conference on Data Mining 2018: Advances in Data Mining. Applications and Theoretical Aspects, pp. 309-324. 2018. Inspiration This is Data Set Characteristics: Multivariate, Sequential, Time-Series, Text

  Language:Jupyter Notebook183

• riccardorinaldi7/FederatedLearning

  Thesis in Federated Learning using an Edge/Cloud Computing architecture

  Language:Python10203

• Firemania/Federated_Learning

  A Federated Learning computing framework for deep learning-based model development by both server and edge devices

  Language:Python700

• JL321/PolicyGradients-torch

  Policy Gradient implementations in pytorch

  Language:Python5202

• yhye97/lms

  LMS using Machine Learning Base Station Selection and Edge Contents Caching

  Language:Jupyter Notebook3

• MoMe36/DoubleDQN

  A repo implementing a PyTorch version of Double DQN

  Language:Python221

• OliverGorges/Federated-learning-on-edge-devices

  Repository for my Bachelor thesis that demonstrates the concept of federated machine learning on edge devices

  Language:Python2