Showing 6 open source projects for "precise"

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    Featuretools

    Featuretools

    An open source python library for automated feature engineering

    An open source Python framework for automated feature engineering. Featuretools automatically creates features from temporal and relational datasets. Featuretools uses DFS for automated feature engineering. You can combine your raw data with what you know about your data to build meaningful features for machine learning and predictive modeling. Featuretools provides APIs to ensure only valid data is used for calculations, keeping your feature vectors safe from common label leakage problems....
    Downloads: 0 This Week
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  • 2
    YOLOv3

    YOLOv3

    Object detection architectures and models pretrained on the COCO data

    Fast, precise and easy to train, YOLOv5 has a long and successful history of real time object detection. Treat YOLOv5 as a university where you'll feed your model information for it to learn from and grow into one integrated tool. You can get started with less than 6 lines of code. with YOLOv5 and its Pytorch implementation. Have a go using our API by uploading your own image and watch as YOLOv5 identifies objects using our pretrained models.
    Downloads: 94 This Week
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  • 3
    Grenade

    Grenade

    Deep Learning in Haskell

    Grenade is a composable, dependently typed, practical, and fast recurrent neural network library for concise and precise specifications of complex networks in Haskell. Because the types are so rich, there's no specific term level code required to construct this network; although it is of course possible and easy to construct and deconstruct the networks and layers explicitly oneself. Networks in Grenade can be thought of as a heterogeneous list of layers, where their type includes not only the layers of the network but also the shapes of data that are passed between the layers. ...
    Downloads: 0 This Week
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  • 4
    MODLEM

    MODLEM

    rule-based, WEKA compatible, Machine Learning algorithm

    ...Actually the nominal and numeric attributes are treated in the same way: attribute's space is being searched to find the best rule condition during rule induction. In result numeric attribute's conditions are more precise and closely describe the class. This algorithm contains some aspects of Rough Set Theory: the class definition can be described accordingly to its lower or upper approximation. For more information, see: Stefanowski, Jerzy. The rough set based rule induction technique for classification problems. In: Proc. 6th European Congress on Intelligent Techniques and Soft Computing, vol. 1. ...
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    Downloads: 18 This Week
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  • 5

    drvq

    dimensionality-recursive vector quantization

    ...It is an approximation of k-means that is practically constant in data size and applies to arbitrarily high dimensions but can only scale to a few thousands of centroids. As a by-product of training, a tree structure performs either exact or approximate quantization on trained centroids, the latter being not very precise but extremely fast. A detailed README file describes the usage of the software, including license, requirements, installation, file formats, sample data, tools, and options. With the sample data provided and the default options, it is possible to test the code immediately as a demo. DRVQ has a 2-clause BSD license. Please refer to the DRVQ software home page, the research project, or the original publication for more information. ...
    Downloads: 0 This Week
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  • 6
    pySTEP or Python Strongly Typed gEnetic Programming: A light Genetic Programming API that allows the user to easily evolve populations of trees with precise grammatical and structural constraints.
    Downloads: 0 This Week
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