Showing 11 open source projects for "learn to code"

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  • 1
    Hello Algorithm

    Hello Algorithm

    Animated illustrations, one-click data structure

    ...The whole book uses animated illustrations, the content is clear and easy to understand, and the learning curve is smooth, guiding beginners to explore the knowledge map of data structures and algorithms. The source code can be run with one click, helping readers improve their programming skills during exercises and understand the working principles of algorithms and the underlying implementation of data structures. Readers are encouraged to help each other learn, and questions and comments can usually be answered within two days.
    Downloads: 1 This Week
    Last Update:
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  • 2
    Rubix ML

    Rubix ML

    A high-level machine learning and deep learning library for PHP

    Rubix ML is a free open-source machine learning (ML) library that allows you to build programs that learn from your data using the PHP language. We provide tools for the entire machine learning life cycle from ETL to training, cross-validation, and production with over 40 supervised and unsupervised learning algorithms. In addition, we provide tutorials and other educational content to help you get started using ML in your projects.
    Downloads: 0 This Week
    Last Update:
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  • 3
    javascript-algorithms

    javascript-algorithms

    JavaScript implementations of computer science algorithms

    ...Because algorithms are implemented in a language (JavaScript) many developers use daily, the repo becomes especially useful for students, interview-preparation candidates, or anyone wanting to learn algorithmic thinking without switching languages. The code tends to be readable, structured, and adapted to JavaScript idioms, making it easier to follow for JS developers.
    Downloads: 0 This Week
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  • 4
    Reinforcement-learning

    Reinforcement-learning

    Implementation of Reinforcement Learning Algorithms. Python, OpenAI

    Reinforcement-learning is a widely used educational repository that provides implementations, exercises, and solutions for a broad range of reinforcement learning algorithms, designed to complement foundational texts and courses in the field. The project collects popular approaches such as dynamic programming, Monte Carlo methods, temporal difference learning, Q-learning, SARSA, deep Q-networks, and policy gradient techniques, often demonstrated with Python and OpenAI Gym environments so...
    Downloads: 2 This Week
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  • 5
    Arduino

    Arduino

    Open-source electronics platform

    Arduino is an open-source physical computing platform based on a simple I/O board and a development environment that implements the Processing/Wiring language. Arduino can be used to develop stand-alone interactive objects or can be connected to software on your computer (e.g. Flash, Processing and MaxMSP). The boards can be assembled by hand or purchased preassembled. Arduino is a popular tool for IoT product development as well as one of the most successful tools for STEM/STEAM education....
    Downloads: 125 This Week
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  • 6
    code-nav

    code-nav

    Professional programming navigation

    Programming Navigation is not just a website, but a complete ecology with the goal of "helping everyone discover high-quality programming resources and improve learning efficiency". It contains multiple sub-projects, and the technology stack includes React, Java SpringBoot, Tencent Cloud Development, etc., all of which are open source for everyone to learn, so that you can easily develop beautiful information navigation websites! Most of the programming navigation websites are in disrepair...
    Downloads: 0 This Week
    Last Update:
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  • 7
    Kalman and Bayesian Filters in Python

    Kalman and Bayesian Filters in Python

    Kalman Filter book using Jupyter Notebook

    ...Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filters, and more. All exercises include solutions. Introductory text for Kalman and Bayesian filters. All code is written in Python, and the book itself is written using Juptyer Notebook so that you can run and modify the code in your browser. What better way to learn? This book teaches you how to solve all sorts of filtering problems. Use many different algorithms, all based on Bayesian probability. In simple terms Bayesian probability determines what is likely to be true based on past information. ...
    Downloads: 1 This Week
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  • 8
    MADDPG

    MADDPG

    Code for the MADDPG algorithm from a paper

    ...The code is built on top of TensorFlow and integrates with the Multiagent Particle Environments (MPE) for benchmarking. Researchers can use it to reproduce the experiments presented in the paper, which demonstrate how agents learn behaviors such as coordination, competition, and communication. Although archived, MADDPG remains a widely cited baseline in multi-agent reinforcement learning research and has inspired further algorithmic developments.
    Downloads: 1 This Week
    Last Update:
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  • 9
    Pygorithm

    Pygorithm

    A Python module for learning all major algorithms

    A Python module to learn all the major algorithms on the go! Purely for educational purposes. If you are using Python 2.7 use pip instead. Depending on your permissions, you might need to use pip install, user pygorithm to install. To see all the available functions in a module, you can just type help() with the module name as an argument.
    Downloads: 0 This Week
    Last Update:
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  • 10
    ProViz
    ProViz is a program visualization framework for Java. It is built as a plugin to the Eclipse IDE. (We are getting close to publishing resources and making this project distributable. Stay tuned!) A video demo: http://www.youtube.com/watch?v=BsoK1n-OM
    Downloads: 0 This Week
    Last Update:
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  • 11
    LSGTL means LLX’s Static Graph Template Library which is a light-weighted header-only template library developed mainly for static graph analysis. LSGTL is expected to be used in laboratories for research purposes mostly.
    Downloads: 0 This Week
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