Showing 7 open source projects for "work"

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  • 1
    GROBID

    GROBID

    A machine learning software for extracting information

    GROBID is a machine learning library for extracting, parsing, and re-structuring raw documents such as PDF into structured XML/TEI encoded documents with a particular focus on technical and scientific publications. First developments started in 2008 as a hobby. In 2011 the tool has been made available in open source. Work on GROBID has been steady as a side project since the beginning and is expected to continue as such. Header extraction and parsing from article in PDF format. The extraction here covers the usual bibliographical information (e.g. title, abstract, authors, affiliations, keywords, etc.). References extraction and parsing from articles in PDF format, around .87 F1-score against on an independent PubMed Central set of 1943 PDF containing 90,125 references, and around .89 on a similar bioRxiv set of 2000 PDF (using the Deep Learning citation model). ...
    Downloads: 9 This Week
    Last Update:
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  • 2
    MIT Deep Learning Book

    MIT Deep Learning Book

    MIT Deep Learning Book in PDF format by Ian Goodfellow

    ...This is not available as PDF download. So, I have taken the prints of the HTML content and bound them into a flawless PDF version of the book, as suggested by the website itself. Printing seems to work best printing directly from the browser, using Chrome. Other browsers do not work as well.
    Downloads: 5 This Week
    Last Update:
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  • 3
    Seldon Server

    Seldon Server

    Machine learning platform and recommendation engine on Kubernetes

    Seldon Server is a machine learning platform and recommendation engine built on Kubernetes. Seldon reduces time-to-value so models can get to work faster. Scale with confidence and minimize risk through interpretable results and transparent model performance. Seldon Core focuses purely on deploying a wide range of ML models on Kubernetes, allowing complex runtime serving graphs to be managed in production. Seldon Core is a progression of the goals of the Seldon-Server project but also a more restricted focus to solving the final step in a machine learning project which is serving models in production. ...
    Downloads: 0 This Week
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  • 4

    FPF_predict

    Fine Particle Fraction (FPF) predictor

    Application implements models described by classical mathematical equation for in vitro deposition prediction based on characteristics of formulation and assay conditions. This work was funded by Poland-Singapore bilateral cooperation project no 2/3/POL-SIN/2012. Published article: https://www.dovepress.com/empirical-modeling-of-the-fine-particle-fraction-fornbspcarrier-based--peer-reviewed-fulltext-article-IJN
    Downloads: 0 This Week
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  • 5

    JCLALwebservice

    Web service for JCLAL

    This work is part of the area of Artificial Intelligence, in particular in the field of machine learning. The web service is built to facilitate the use of JCLAL in applications developed in any programming language. Users should know only the basic format to send and receive requests.
    Downloads: 0 This Week
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  • 6

    NN Image Recognition (with source-code)

    This is ANN trained application to predict digits from 0 - 9.

    ...I trained ANN with 100 samples of each digit. It takes input of 20x20 pixel image and predicts it with Neural Network. It may predict wrong digit due to very low sample data but it work 90% correctly. Note: JRE 1.6 is required to run this application.
    Downloads: 0 This Week
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  • 7

    Cinefile

    A category-based approach to exploring film data.

    ...It allows the user to identify abstract categories of films by providing examples of category members, learns to classify films as belonging or not belonging to those categories, and provides a graphical interface for exploring and comparing categories. Cinefile is designed to work with data retrieved from the Internet Movie Database (imdb.com). This data is used for classification and is the subject of the category-based analysis. Cinefile was developed by the University of Mary Washington's Computer Science department (http://cas.umw.edu/computerscience).
    Downloads: 0 This Week
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