Showing 7 open source projects for "updates"

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  • AI-generated apps that pass security review Icon
    AI-generated apps that pass security review

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  • 1
    DifferenceKit

    DifferenceKit

    A fast and flexible O(n) difference algorithm framework

    A fast and flexible O(n) difference algorithm framework for Swift collection. The algorithm is optimized based on the Paul Heckel’s algorithm. This is a diffing algorithm developed for Carbon, works stand alone. The algorithm optimized based on the Paul Heckel’s algorithm. See also his paper A technique for isolating differences between files released in 1978. It allows all kind of diffs to be calculated in linear time O(n). RxDataSources and IGListKit are also implemented based on his...
    Downloads: 0 This Week
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  • 2
    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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  • 3
    Machine Learning Cheat Sheet

    Machine Learning Cheat Sheet

    Classical equations and diagrams in machine learning

    This repository is a visually rich and well-organized “cheat sheet” summarizing core machine learning concepts, algorithms, formulas, and best practices. It includes summaries of supervised and unsupervised learning methods, model evaluation metrics (accuracy, precision, recall, ROC/AUC), overfitting/underfitting, regularization (L1/L2), cross-validation, feature engineering techniques, and perhaps tips for hyperparameter tuning. Each section is presented concisely, often with diagrams,...
    Downloads: 0 This Week
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  • 4
    Dopamine

    Dopamine

    Framework for prototyping of reinforcement learning algorithms

    ...This first version focuses on supporting the state-of-the-art, single-GPU Rainbow agent (Hessel et al., 2018) applied to Atari 2600 game-playing (Bellemare et al., 2013). Specifically, our Rainbow agent implements the three components identified as most important by Hessel et al., n-step Bellman updates, prioritized experience replay, and distributional reinforcement learning. For completeness, we also provide an implementation of DQN (Mnih et al., 2015). For additional details, please see our documentation. We provide a set of Colaboratory notebooks which demonstrate how to use Dopamine. We provide a website which displays the learning curves for all the provided agents, on all the games.
    Downloads: 0 This Week
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  • Cut Cloud Costs with Google Compute Engine Icon
    Cut Cloud Costs with Google Compute Engine

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  • 5
    **NOTE**: The WALA project is now hosted at https://github.com/wala/WALA; the latest source code and updates are available there. The code here on Sourceforge is highly out of date and unsupported. The T. J. Watson Libraries for Analysis (WALA) provide program analysis capabilities for Java bytecode and related languages. The WALA libraries constitute an open-source release of parts of IBM Research's DOMO analysis infrastructure.
    Downloads: 0 This Week
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  • 6

    RSSA

    Rejection-based stochastic simulation algorithm (RSSA)

    Rejection-based stochastic simulation algorithm (RSSA) is an efficient exact algorithm for doing stochastic simulation of biochemical reaction systems. RSSA improves over state of the art of stochastic simulations by avoiding and collapsing as much the number of propensity updates.
    Downloads: 0 This Week
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  • 7
    Asteroid

    Asteroid

    A CBLS Engine in SCALA, NOW part of OscaR

    Asteroid has been merged with Scampi, to give rise to OScar. Follow us on https://bitbucket.org/oscarlib/oscar/wiki/Home Asteroid offers a powerful framework for developing constraint-based local search solution to combinatorial problems. This technique provides good scalability to real-world problems. It includes a library of standard constraints and invariants to declaratively define the problem you want to solve, and it also provides powerful search mechanisms.
    Downloads: 0 This Week
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