Showing 32 open source projects for "type"

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  • 1
    anaGo

    anaGo

    Bidirectional LSTM-CRF and ELMo for Named-Entity Recognition

    ...Unlike traditional sequence labeling solver, anaGo doesn't need to define any language-dependent features. Thus, we can easily use anaGo for any language. In anaGo, the simplest type of model is the Sequence model. Sequence model includes essential methods like fit, score, analyze and save/load. For more complex features, you should use the anaGo modules such as models, preprocessing and so on.
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  • 2
    Grenade

    Grenade

    Deep Learning 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. To perform back propagation, one can call the eponymous function which takes a network, appropriate input, and target data, and returns the back propagated gradients for the network. The shapes of the gradients are appropriate for each layer and may be trivial for layers like Relu which have no learnable parameters.
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  • 3
    HLearn

    HLearn

    Homomorphic machine learning

    HLearn is a Haskell-based machine learning library focused on composability, algebraic structure, and performance. It provides a functional approach to building machine learning algorithms by leveraging algebraic properties such as monoids and groups. This allows for parallel, incremental, and distributed computation in a mathematically consistent way. HLearn aims to provide implementations of common algorithms like k-means, naive Bayes, and others while maintaining the expressiveness and...
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  • 4

    Unsupervised Random Forest

    On-line Unsupervised Random Forest

    ...It supports on-line prediction of new observations (no need to retrain); and supports datasets that contain both continuous (e.g. CPU load) and categorical (e.g. VM instance type) features. In particular, we use an unsupervised formulation of the Random Forest algorithm to calculate similarities and provide them as input to a clustering algorithm. For the sake of efficiency and meeting the dynamism requirement of autonomic clouds, our methodology consists of two steps: (i) off-line clustering and (ii) on-line prediction. ...
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  • 5
    Consilium Sentence Suggestions Tools

    Consilium Sentence Suggestions Tools

    Consilium – User Defined sentence Suggestion Tool.

    ...While our aim is to provide a sentence suggestion tool which will give suggestion to complete the sentence according previously enterd data by the user. Output or suggestion for same sentence or word will change person to person according to previously entered data by that person. So, it will be very easy to type any document, sms, mail, chatting etc.
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  • 6
    RoboBeans is an interface to the "Robocup 2D Soccer Simulation Server" that allows developers to write Robocup teams\agents concentrating on behaviour and AI without having to worry about syntax of communication or network issues.
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  • 7
    A packet dissector driven by machine learning algorithms. You train it to recognize specific types of packets by showing it examples and counterexamples of some packet type, and it will figure out which bits in the packet define it as the type you seek.
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