Showing 4 open source projects for "network check"

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    MongoDB Atlas runs apps anywhere

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    Thinc

    Thinc

    A refreshing functional take on deep learning

    Thinc is a lightweight deep learning library that offers an elegant, type-checked, functional-programming API for composing models, with support for layers defined in other frameworks such as PyTorch, TensorFlow and MXNet. You can use Thinc as an interface layer, a standalone toolkit or a flexible way to develop new models. Previous versions of Thinc have been running quietly in production in thousands of companies, via both spaCy and Prodigy. We wrote the new version to let users compose,...
    Downloads: 8 This Week
    Last Update:
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  • 2
    UnBBayes

    UnBBayes

    Framework & GUI for Bayes Nets and other probabilistic models.

    Check out the license section (https://sourceforge.net/p/unbbayes/wiki/License/) for our licensing policy.
    Downloads: 5 This Week
    Last Update:
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  • 3
    ManimML

    ManimML

    ManimML is a project focused on providing animations

    ManimML is a project focused on providing animations and visualizations of common machine-learning concepts with the Manim Community Library. Please check out our paper. We want this project to be a compilation of primitive visualizations that can be easily combined to create videos about complex machine-learning concepts. Additionally, we want to provide a set of abstractions that allow users to focus on explanations instead of software engineering.
    Downloads: 0 This Week
    Last Update:
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  • 4
    PyTorch Natural Language Processing

    PyTorch Natural Language Processing

    Basic Utilities for PyTorch Natural Language Processing (NLP)

    ...It’s built with the very latest research in mind, and was designed from day one to support rapid prototyping. PyTorch-NLP comes with pre-trained embeddings, samplers, dataset loaders, metrics, neural network modules and text encoders. It’s open-source software, released under the BSD3 license. With your batch in hand, you can use PyTorch to develop and train your model using gradient descent. For example, check out this example code for training on the Stanford Natural Language Inference (SNLI) Corpus. Now you've setup your pipeline, you may want to ensure that some functions run deterministically. ...
    Downloads: 0 This Week
    Last Update:
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