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ailearning

AiLearning：数据分析+机器学习实战+线性代数+PyTorch+NLTK+TF2

annotated_deep_learning_paper_implementations

🧑‍🏫 59 Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠

ardupilot

ArduPlane, ArduCopter, ArduRover, ArduSub source

bearmod

Basic Epidemic, Activity, and Response COVID-19 model

breast-cancer-prognosis

Config files for my GitHub profile.

correlates_processing

Generalized reproducible reporting workflow for immune correlates data processing in vaccine efficacy trials

correlates_reporting2

Generalized reproducible reporting workflow for statistical analyses of candidate immune correlates of risk and protection in vaccine efficacy trials

correlates_reporting_usgcove_archive

Reproducible reporting workflows for the immune correlates statistical analyses of the Moderna and Janssen COVID-19 vaccine efficacy trials by the USG/COVE Response Biostatistics Team [Archived 16 October 2021]

cs-self-learning

计算机自学指南

daa-analysis-tools

Toolkit for analyzing ACAS-Xu and DAIDALUS UAS Detect and Avoid (DAA) pilot guidance using Live Virtual Constructive (LVC) and GPS logs.

daidalus

open source release: LAR-19282-1 Detect and Avoid Alerting Logic for Unmanned Systems (DAIDALUS) with Dynamic Well-Clear Separation Volumes).

ddp-gym

Differential Dynamic Programming controller operating in OpenAI Gym environment.

deeplearningpython35

neuralnetworksanddeeplearning.com integrated scripts for Python 3.5.2 and Theano with CUDA support

dive-into-deep-learning-pytorch-pdf

本项目对中文版《动手学深度学习》中的代码进行了PyTorch实现并整理为PDF版本供下载

double-oracle

double_oracle

double_oracle-1

dppackagemod

Modification of the DPpackage to handle measurement error

dqn-multi-agent-rl

Deep Q-learning (DQN) for Multi-agent Reinforcement Learning (RL)

drl

Deep Reinforcement Learning

evatargetedcovid19testing

Open-Source code for Project Eva: A Targeted Testing Protocol for Greece

flightradar24

Data library for Flight Radar 24 written in Python 3.

game-theoretic-network-simulator

GTNS is a discrete-event network simulator targeted primarily for research and educational use. GTNS is written in Visual C++ programming language and supports different network topologies. This simulator was first produced to implement locally multipath adaptive routing (LMAR) protocol, classified as a new reactive distance vector routing protocol for MANETs. LMAR can find an ad-hoc path without selfish nodes and wormholes using an exhaustive search algorithm in polynomial time. Also when the primary path fails, it discovers an alternative safe path if network graph remains connected after eliminating selfish/malicious nodes. The key feature of LMAR to seek safe route free of selfish and malicious nodes in polynomial time is its searching algorithm and flooding stage that its generated traffic is equi-loaded compared to single-path routing protocols but its security efficiency to bypass the attacks is much better than the other multi-path routing protocols. LMAR concept is introduced to provide the security feature known as availability and a simulator has been developed to analyze its behavior in complex network environments [1]. Then we have added detection mechanism to the simulator, which can detect selfish nodes in network. The proposed algorithm is resilient against collision and can be used in networks which wireless nodes use directional antennas and it also defend against an attack that malicious nodes try to break communications by relaying the packets in a specific direction. Some game theoretic strategies to enforce cooperation in network have been implemented in GTNS, for example Forwarding-Ratio Strategy, TFT-Strategy and ERTFT. This tutorial helps new users to get familiar with GTNS and run different network scenarios.

game_theory

Implementing Algorithms for Computing Stackelberg Equilibria in Security Games

gvgai

This is the framework for the General Video Game Competition - http://www.gvgai.net/

hands-on-rl

https://hrl.boyuai.com/

infinite

machine-learning-in-action-python3

《机器学习实战》的python3源码

machine-learning-toy-code

《机器学习》（西瓜书）代码实战

markovgamesolvers

This is code for finding the minimax/nash/stackelberg strategy of players in Markov Games.