Search Results for "open-lates"
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Showing 846 open source projects for "open-lates"
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Secure remote access solution to your private network, in the cloud or on-prem.OpenVPN is here to bring simple, flexible, and cost-effective secure remote access to companies of all sizes, regardless of where their resources are located.
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TensorFlow.js
TensorFlow.js is a library for machine learning in JavaScript
TensorFlow.js is a library for machine learning in JavaScript. Develop ML models in JavaScript, and use ML directly in the browser or in Node.js. Use off-the-shelf JavaScript models or convert Python TensorFlow models to run in the browser or under Node.js. Retrain pre-existing ML models using your own data. Build and train models directly in JavaScript using flexible and intuitive APIs. Tensors are the core datastructure of TensorFlow.js They are a generalization of vectors and matrices to... -
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Mongoose
Elegant mongodb object modeling for node.js
Mongoose is a MongoDB object modeling tool that was built to answer the need for better ways to model your application data. It's designed to work in an asynchronous environment, providing a simple, straightforward approach to object modeling that skips out on the tedious tasks of writing MongoDB validation, casting and business logic boilerplate. Mongoose offers an uncomplicated schema-based solution, and comes with nifty features like type casting, validation, query building, and business... -
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statsmodels
Statsmodels, statistical modeling and econometrics in Python
statsmodels is a Python module that provides classes and functions for the estimation of many different statistical models, as well as for conducting statistical tests, and statistical data exploration. An extensive list of result statistics are available for each estimator. The results are tested against existing statistical packages to ensure that they are correct. The package is released under the open source Modified BSD (3-clause) license. Generalized linear models with support for all... -
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torchvision
Datasets, transforms and models specific to Computer Vision
The torchvision package consists of popular datasets, model architectures, and common image transformations for computer vision. We recommend Anaconda as Python package management system. Torchvision currently supports Pillow (default), Pillow-SIMD, which is a much faster drop-in replacement for Pillow with SIMD, if installed will be used as the default. Also, accimage, if installed can be activated by calling torchvision.set_image_backend('accimage'), libpng, which can be installed via... -
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Constructs
Define composable configuration models through code
Constructs are classes that define a "piece of system state". Constructs can be composed together to form higher-level building blocks which represent a more complex state. Constructs are often used to represent the desired state of cloud applications. For example, in the AWS CDK, which is used to define the desired state for AWS infrastructure using CloudFormation, the lowest-level construct represents a resource definition in a CloudFormation template. These resources are composed to... -
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SageMaker Hugging Face Inference Toolkit
Library for serving Transformers models on Amazon SageMaker
SageMaker Hugging Face Inference Toolkit is an open-source library for serving Transformers models on Amazon SageMaker. This library provides default pre-processing, predict and postprocessing for certain Transformers models and tasks. It utilizes the SageMaker Inference Toolkit for starting up the model server, which is responsible for handling inference requests. For the Dockerfiles used for building SageMaker Hugging Face Containers, see AWS Deep Learning Containers. The SageMaker Hugging... -
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smclarify
Fairness aware machine learning. Bias detection and mitigation
Fairness Aware Machine Learning. Bias detection and mitigation for datasets and models. A facet is column or feature that will be used to measure bias against. A facet can have value(s) that designates that sample as "sensitive". Bias detection and mitigation for datasets and models. The label is a column or feature which is the target for training a machine learning model. The label can have value(s) that designates that sample as having a "positive" outcome. A bias measure is a function... -
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OrientDB
DBMS supporting graph, document, full-text and geospatial models
OrientDB is an Open Source Multi-Model NoSQL DBMS with the support of Native Graphs, Documents, Full-Text search, Reactivity, Geo-Spatial and Object Oriented concepts. It's written in Java and it's amazingly fast. No expensive run-time JOINs, connections are managed as persistent pointers between records. You can traverse thousands of records in no time. Supports schema-less, schema-full and schema-mixed modes. Has a strong security profiling system based on user, roles and predicate security... -
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Laravel Media Library
Associate files with Eloquent models
This package can associate all sorts of files with Eloquent models. It provides a simple, fluent API to work with. The Pro version of the package offers Blade, Vue and React components to handle uploads to the media library and to administer the content of a medialibrary collection. The storage of the files is handled by Laravel's Filesystem, so you can use any filesystem you like. Additionally the package can create image manipulations on images and pdfs that have been added in the media... -
Save hundreds of developer hours with components built for SaaS applications.Whether you want full self-service analytics or simpler multi-tenant security, Qrvey’s embeddable components and scalable data management remove the guess work.
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MJExtension
A conversion framework between JSON and model
A fast, convenient and nonintrusive conversion framework between JSON and model. Dictionary-to-model framework with fast conversion speed and easy to use. A large number of unit tests have been carried out on various commonly used data scenarios. MJExtension is an ultra-lightweight framework that converts between dictionaries and models. Coding all properties of a model with only one line of code. Coding (archiving and unarchiving) of all attributes of the model can be achieved with just one... -
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The GR module for Julia
Plotting for Julia based on GR
This is the GR module for Julia. It places a Julia interface to GR, a universal framework for visualization applications. GR allows users to create high quality, engaging visualizations, everything from 2D graphs to complex 3D scenes. With this module simply type in Julia 'using gr', and you can instantly start calling functions in the GR framework API. GR is based on an implementation of a Graphical Kernel System (GKS) and OpenGL. As a self-contained system, integration into existing... -
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SageMaker Training Toolkit
Train machine learning models within Docker containers
Train machine learning models within a Docker container using Amazon SageMaker. Amazon SageMaker is a fully managed service for data science and machine learning (ML) workflows. You can use Amazon SageMaker to simplify the process of building, training, and deploying ML models. To train a model, you can include your training script and dependencies in a Docker container that runs your training code. A container provides an effectively isolated environment, ensuring a consistent runtime and... -
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AWS Deep Learning Containers
A set of Docker images for training and serving models in TensorFlow
AWS Deep Learning Containers (DLCs) are a set of Docker images for training and serving models in TensorFlow, TensorFlow 2, PyTorch, and MXNet. Deep Learning Containers provide optimized environments with TensorFlow and MXNet, Nvidia CUDA (for GPU instances), and Intel MKL (for CPU instances) libraries and are available in the Amazon Elastic Container Registry (Amazon ECR). The AWS DLCs are used in Amazon SageMaker as the default vehicles for your SageMaker jobs such as training, inference,... -
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TensorFlow Model Garden
Models and examples built with TensorFlow
The TensorFlow Model Garden is a repository with a number of different implementations of state-of-the-art (SOTA) models and modeling solutions for TensorFlow users. We aim to demonstrate the best practices for modeling so that TensorFlow users can take full advantage of TensorFlow for their research and product development. To improve the transparency and reproducibility of our models, training logs on TensorBoard.dev are also provided for models to the extent possible though not all models... -
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Laravel MongoDB
MongoDB based eloquent model and Query builder for Laravel
This package adds functionalities to the Eloquent model and Query builder for MongoDB, using the original Laravel API. This library extends the original Laravel classes, so it uses exactly the same methods. Make sure you have the MongoDB PHP driver installed. In case your Laravel version does NOT autoload the packages, add the service provider to config/app.php. For usage with Lumen, add the service provider in bootstrap/app.php. In this file, you will also need to enable Eloquent. You must... -
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PyMC3
Probabilistic programming in Python
PyMC3 allows you to write down models using an intuitive syntax to describe a data generating process. Fit your model using gradient-based MCMC algorithms like NUTS, using ADVI for fast approximate inference — including minibatch-ADVI for scaling to large datasets, or using Gaussian processes to build Bayesian nonparametric models. PyMC3 includes a comprehensive set of pre-defined statistical distributions that can be used as model building blocks. Sometimes an unknown parameter or variable... -
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redux-saga
An alternative side effect model library for Redux applications
redux-saga is a library that aims to make application side effects (i.e. asynchronous things like data fetching and impure things like accessing the browser cache) easier to manage, more efficient to execute, easy to test, and better at handling failures. The mental model is that a saga is like a separate thread in your application that's solely responsible for side effects. redux-saga is a redux middleware, which means this thread can be started, paused and cancelled from the main... -
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DeepCTR-Torch
Easy-to-use,Modular and Extendible package of deep-learning models
DeepCTR-Torch is an easy-to-use, Modular and Extendible package of deep-learning-based CTR models along with lots of core components layers that can be used to build your own custom model easily.It is compatible with PyTorch.You can use any complex model with model.fit() and model.predict(). With the great success of deep learning, DNN-based techniques have been widely used in CTR estimation tasks. The data in the CTR estimation task usually includes high sparse,high cardinality categorical... -
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PlantUml allows to quickly create some UML diagram using a simple textual description language.
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Open tool platform for the cost effective rigorous development of dependable complex software systems services. This platform is based on the event-B formal method and provides natural support for refinement and mathematical proof.
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Violet is a UML editor with these benefits: Very easy to learn and use. Draws nice-looking diagrams. Completely free. Cross-platform. Violet is intended for developers, students, teachers, and authors who need to produce simple UML diagrams quickly. Want to contribute ? Source code is hosted on github : https://github.com/violetumleditor/violetumleditor
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JGraphT is a free Java class library that provides mathematical graph-theory objects and algorithms. JGraphT supports a rich gallery of graphs and is designed to be powerful, extensible, and easy to use.
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Yaoqiang BPMN Editor
an Open Source BPMN 2.0 / DMN 1.1 Modeler
Yaoqiang BPMN Editor is a graphical editor for business process diagrams, compliant with OMG specifications (BPMN 2.0 / DMN 1.1). -
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Linguistic Tree Constructor
Syntax tree editor for rapid annotation of existing text
Linguistic Tree Constructor (LTC) is a tool for drawing lingusitic syntax trees of already-existing text. It is a syntax editor, not a text editor, so the text has to exist already. It is best suited for large-scale, rapid creation of hand-annotated treebanks. The user can define their own node categories, and can label each node with labels, also definable by the user. LTC supports "generic", X-Bar and RRG trees. Supports interlinear texts in SIL SFM format. -
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NOTE: The GROOVE codebase has moved to https://github.com/nl-utwente-groove