Showing 4 open source projects for "joy 2 key"

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  • 1
    Bootstrap Your Own Latent (BYOL)

    Bootstrap Your Own Latent (BYOL)

    Usable Implementation of "Bootstrap Your Own Latent" self-supervised

    ...This repository offers a module that one can easily wrap any image-based neural network (residual network, discriminator, policy network) to immediately start benefitting from unlabelled image data. There is now new evidence that batch normalization is key to making this technique work well. A new paper has successfully replaced batch norm with group norm + weight standardization, refuting that batch statistics are needed for BYOL to work. Simply plugin your neural network, specifying (1) the image dimensions as well as (2) the name (or index) of the hidden layer, whose output is used as the latent representation used for self-supervised training.
    Downloads: 1 This Week
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  • 2
    PostgresML

    PostgresML

    The GPU-powered AI application database

    PostgresML is a complete platform in a PostgreSQL extension. Build simpler, faster, and more scalable models right inside your database. Explore the SDK and test open source models in our hosted database. Combine and automate the entire workflow from embedding generation to indexing and querying for the simplest (and fastest) knowledge-based chatbot implementation. Leverage multiple types of natural language processing and machine learning models such as vector search and personalization...
    Downloads: 0 This Week
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  • 3
    Oryx

    Oryx

    Lambda architecture on Apache Spark, Apache Kafka for real-time

    Oryx 2 is a realization of the lambda architecture built on Apache Spark and Apache Kafka, but with specialization for real-time large-scale machine learning. It is a framework for building applications but also includes packaged, end-to-end applications for collaborative filtering, classification, regression and clustering. The application is written in Java, using Apache Spark, Hadoop, Tomcat, Kafka, Zookeeper and more.
    Downloads: 1 This Week
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  • 4

    OWL Machine Learning

    Machine learning algorithm using OWL

    Feature construction and selection are two key factors in the field of Machine Learning (ML). Usually, these are very time-consuming and complex tasks because the features have to be manually crafted. The features are aggregated, combined or split to create features from raw data. This project makes use of ontologies to automatically generate features for the ML algorithms.
    Downloads: 0 This Week
    Last Update:
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    Automate contact and company data extraction

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