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Tutorials and programming exercises for learning Q# and quantum computing
Wireless Sensor Network Simulator
A simulation environment is created in MATLAB using Automated Driving Toolbox and then vehicle tracking is done using Kalman filter utilizing simulated RADAR data for measurement update
Detect Vehicle range and angle from radar noisy measurements.
Tomotherapy Dose Optimization according to Shepard's 1999 paper
Random Forest hyper-parameter fine tuning with a gentic algorithm
Range Image-based LiDAR Localization for Autonomous Vehicles Using Mesh Maps (chen2021icra)
Detection of rare-events from a time-series dataset using both LSTM autoencoders and also a normal LSTM network.
Code for "Causal inference in the context of an error prone exposure: Air pollution and mortality". A new approach for estimating causal effects when the exposure is measured with error.
Driving directions with google maps integration
Using Hierarchical Temporal Memory for real time anomaly prediction in distributed systems
A real-time interactive web app based on data pipelines using streaming Twitter data, automated sentiment analysis, and MySQL&PostgreSQL database (Deployed on Heroku)
Code for Recurrent Spatial Transformer Networks
It is the code of my IEEE conference paper on refined rough fuzzy clustering.
What is reinforcement learning? Reinforcement learning is the training of machine learning models to make a sequence of decisions. The agent learns to achieve a goal in an uncertain, potentially complex environment. In reinforcement learning, an artificial intelligence faces a game-like situation. The computer employs trial and error to come up with a solution to the problem. To get the machine to do what the programmer wants, the artificial intelligence gets either rewards or penalties for the actions it performs. Its goal is to maximize the total reward. Although the designer sets the reward policy–that is, the rules of the game–he gives the model no hints or suggestions for how to solve the game. It’s up to the model to figure out how to perform the task to maximize the reward, starting from totally random trials and finishing with sophisticated tactics and superhuman skills. By leveraging the power of search and many trials, reinforcement learning is currently the most effective way to hint machine’s creativity. In contrast to human beings, artificial intelligence can gather experience from thousands of parallel gameplays if a reinforcement learning algorithm is run on a sufficiently powerful computer infrastructure.
Reinforce Learing, Q-Rounting, Shortest-Path
Code for the "Relational Neural Expectation Maximization: Unsupervised Discovery of Objects and their Interactions" paper.
ACL 2019: Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs
A PyTorch implementation of OpenAI's REPTILE algorithm
Handing Wang, Yaochu Jin, A Random Forest-Assisted Evolutionary Algorithm %for Data-Driven Constrained Multiobjective Combinatorial Optimization of Trauma Systems IEEE Transactions on Cybernetics, accept, 2018.
• Data preprocessing and Data base management using MySQL. • Coverage Analysis using SVR (Support Vector Regression), ANN (Artificial Neural Network) with Keras, Tensor Flow and Outlier Detection through cluster-based approaches including K-Means clustering and DBSCAN • Network Health Estimation using Bayesian Kriging, Deep Learning and Graph Signal Processing by solving an optimization problem on graph using proximal splitting methods. • Developing a user-friendly GUI for the regularity authority for data visualization, analytics and store the results of the analysis
This package contains a collection of tools which implements different regression methods in order to localize and track an agent in an environment
Radio Frequency based localization (@433MHz) framework for micro underwater robots in confined tanks using software defined radio (SDR)
Training agent with reinforcement learning and intrinsic reward to navigate through complex environment
RL- & Neural network enabled-agent to accept a request with optimum reward
Reinforcement Learning in continuous state and action spaces. DDPG: Deep Deterministic Policy Gradient and A3C: Asynchronous Actor-Critic Agents
Python3 library able to connect the RLLIB framework with the SUMO simulator.
RNN based Time-series Anomaly detector model implemented in Pytorch.
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.