Search Results for "statistics" - Page 20
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Showing 1885 open source projects for "statistics"
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HttpReports APM
HttpReports is an APM (application performance monitor) system
HttpReports is an APM (application performance monitor) system for Net Core. Based on MIT open-source protocol, The main functions include statistics, analysis, visualization, monitoring, tracking, etc. It is very suitable for use in microservices. -
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swirl
Learn R, in R
swirl is an R package that allows interactive, in-R learning of statistics, data science, R programming etc. The idea is that you load swirl in R, and it presents you with lessons (within R’s console or RStudio) that ask you to type commands, check results, and progress through tutorial material—without leaving the R environment. It is used for teaching R, especially for beginners, as well as for self-paced learning of packages, data manipulation, visualization, etc. -
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EverydayWechat
Python tool that automates WeChat messages, replies, & group utilities
...In addition to personal messaging automation, the project includes a group assistant that can respond to queries and provide useful information within chat groups. These group utilities can retrieve data such as weather conditions, calendar details, garbage classification information, movie box office statistics, delivery tracking updates, and air quality reports. -
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Manifold ML
A model-agnostic visual debugging tool for machine learning
Manifold is a model-agnostic visual debugging tool for machine learning. Understanding ML model performance and behavior is a non-trivial process, given the intrisic opacity of ML algorithms. Performance summary statistics such as AUC, RMSE, and others are not instructive enough to identify what went wrong with a model or how to improve it. As a visual analytics tool, Manifold allows ML practitioners to look beyond overall summary metrics to detect which subset of data a model is inaccurately predicting. Manifold also explains the potential cause of poor model performance by surfacing the feature distribution difference between better and worse-performing subsets of data. -
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...Maarch includes scan connectors, mailroom management, workflow. Maarch provides handy features like content management, version control, workflow, office templates, statistics, etc. Donwload Maarch Courrier from ower gitlab : https://labs.maarch.org/
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rpackage conjurer
Synthetic data generation using R
Builds synthetic data applicable across multiple domains. This package also provides flexibility to control data distribution to make it relevant to many industry examples. -
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PRMLT
Matlab code of machine learning algorithms in book PRML
...There is no external dependency. This package requires Matlab R2016b or latter, since it utilizes a new Matlab syntax called Implicit expansion (a.k.a. broadcasting). It also requires Statistics Toolbox (for some simple random number generator) and Image Processing Toolbox (for reading image data). The code is extremely compact. Minimizing code length is a major goal. As a result, the core of the algorithms can be easily spotted. Many tricks for speeding up Matlab code are applied (e.g. vectorization, matrix factorization, etc.). ... -
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node-cache
a node internal (in-memory) caching module
node-cache is a simple and efficient in-memory caching library for Node.js, designed for lightweight applications that need fast, temporary data storage without relying on external databases. -
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VoiceShot API - CF SDK
ColdFusion SDK for the VoiceShot API.
VoiceShot's API allows you to quickly integrate both incoming and outgoing phone calling and text messaging services into your applications. From your own applications, you can easily place and receive interactive telephone calls and text messages. Put callers in touch with the data and people they want when they want it. Send notification phone calls and text messages, automate customer service calls/texts, provide order status and integrate with your own custom applications to provide... -
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VoiceShot API - PHP SDK
PHP SDK for processing phone calls and SMS through the VoiceShot API.
VoiceShot's API allows you to quickly integrate both incoming and outgoing phone calling and text messaging services into your applications. From your own applications, you can easily place and receive interactive telephone calls and text messages. Put callers in touch with the data and people they want when they want it. Send notification phone calls and text messages, automate customer service calls/texts, provide order status and integrate with your own custom applications to provide... -
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VoiceShot API - .NET SDK
.NET SDK for processing phone calls and SMS through the VoiceShot API.
VoiceShot's API allows you to quickly integrate both incoming and outgoing phone calling and text messaging services into your applications. From your own applications, you can easily place and receive interactive telephone calls and text messages. Put callers in touch with the data and people they want when they want it. Send notification phone calls and text messages, automate customer service calls/texts, provide order status and integrate with your own custom applications to provide... -
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VoiceShot API - ASP SDK
ASP SDK for processing phone calls and SMS through the VoiceShot API.
VoiceShot's API allows you to quickly integrate both incoming and outgoing phone calling and text messaging services into your applications. From your own applications, you can easily place and receive interactive telephone calls and text messages. Put callers in touch with the data and people they want when they want it. Send notification phone calls and text messages, automate customer service calls/texts, provide order status and integrate with your own custom applications to provide... -
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astLib is a set of python modules that provides some tools for research astronomers. It can be used for simple plots, statistics, common calculations, coordinate conversions, and manipulating FITS images with World Coordinate System (WCS) information.
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VPTree
Implementation of Vantage-Point Tree
How to use: 1-Create a class that inherits vptree.Point 2-Create vptree.VPTree 3-Add points to VPTree by using VPTree.addPoint 4-Call VPTree.mount 5-Search points near to a point of interest by using VPTree.searchNodes, this method returns nodes that contains points. 6-For each node, get the points by calling vptree.Node.getPoints. Restrictions: 1-You can add point to the tree, but can not remove them 2-After you mount the tree, the statistics will not be calculated again. If the tree becomes unbalanced, you must recreate it. Use vptree.VPTree.calculateStats.toString() to check the statistics. -
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Image Quality Assessment
Convolutional Neural Networks to predict aesthetic quality of images
...The goal of the project is to automatically evaluate images based on perceived quality factors such as composition, clarity, and visual appeal. Instead of relying on simple image statistics, the system learns patterns that correlate with human judgments about image aesthetics and technical quality. The repository includes code for training models, performing inference, and evaluating predicted scores against labeled datasets. It also provides utilities for image preprocessing and data management that help prepare datasets for training deep learning models. -
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easy_profiler
Lightweight profiler library for c++
...Context switch information includes duration, target thread ID, thread owner process ID, and thread owner process name. You can see the results of measuring in a simple GUI application which provides full statistics and renders a beautiful time-line. -
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nlp-roadmap
ROADMAP(Mind Map) and KEYWORD for students
...It organizes essential keywords into semantic mind maps rather than presenting a conventional textbook or code library. The material begins with probability and statistics, then progresses through machine learning and text mining. Its final section maps major concepts and methods within natural language processing. The diagrams are intended to suggest a study path, while acknowledging that relationships between topics can be interpreted in different ways. The repository can be used as a curriculum-planning reference and accepts community corrections and alternative perspectives. -
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Artica Statistics Appliance for Squid
Global Management console for manage multiple Squid proxys
Artica Statistics Appliance for Squid is a set of modules that allows you to enable Webfiltering, statistics and manage several Squid proxy from an unique Web management console. With this project you will be able to install TinyProxy act has the client and the Web statistics Appliance act has the central server -
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TheoremJS
A Math library for computation in JavaScript
TheoremJS is a mathematical framework that is packed up with tons of useful mathematical functions. It is composed of functions in most mathematical fields such as algebra, number theory, statistics, etc... It was designed to fit your need, whatever you are trying to do. With TheoremJS, you won't feel the need of running the code on a server, you can run it right on the browser, or on a NodeJS notebook (Jupyter like). Even though we loose performances with BigNumber, our algorithms are well-engineered and ensure decent performances on every device. ... -
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Log@min
centralized syslog-ng monitoring frontend writen in php
Webinterface to monitor many Syslog-ng - Linux Hosts on a central logserver. Powered by SphinxSE for ultrafast Fulltext-Search Queries. Testet with huge amount of entries (over 80 000 000) with incredible good performance. Easy to setup -
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X-DeepLearning
An industrial deep learning framework for high-dimension sparse data
...Storage and communication optimization, parameters are automatically allocated globally without manual intervention, and requests are merged to completely eliminate computing/storage/communication hotspots of ps. Complete streaming training features including feature admission, feature elimination, model incremental export, feature counting statistics, etc. Background: XDL1.0 focuses on throughput optimization and adopts the one request per thread processing model, which can significantly improve the limit throughput under ultra-high concurrency. -
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Open Intro Statistics
An open-source textbook written at the college level
OpenIntro Statistics is a dynamic take on the traditional curriculum, being successfully used at Community Colleges to the Ivy League. Each chapter's content is in one of the eight chapter folders that start with "ch_". Within each folder, there is a "figures" folder and a "TeX" folder. The TeX folder contains the text files that are used to typeset the chapters in the textbook. -
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Manhali is a free, open source Learning Management System (LMS). It is installable and multi-language. Licensed under the GNU-GPL 3 and written in PHP and MySQL. Manhali can track and evaluate learners' behavior by clustering all learners in profiles (A, B, C, D and E) according to their behavior on the platform to allow educators to personalize courses for each profile.
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KeePass Custom Icon Dashboarder
KeePass Plugin to download and manage custom icons
Provides some statistics and management features for custom icons in KeePass. THIS PROJECT HAS MOVED TO https://github.com/incognito1234/Keepass-Custom-Icon-Dashboarder High definition of custom icon can be uploaded. URLs of associated entries are used to retrived the highest definition of icons. For each custom icon, the following information is available: - Number and list of entries that use it - Number and list of groups that use it Mass update features are: - remove custom icons - modify custom icons of groups and entries -
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Coach
Enables easy experimentation with state of the art algorithms
...The available environments allow testing the agent in different fields such as robotics, autonomous driving, games and more. It exposes a set of easy-to-use APIs for experimenting with new RL algorithms and allows simple integration of new environments to solve. Coach collects statistics from the training process and supports advanced visualization techniques for debugging the agent being trained. Coach supports many state-of-the-art reinforcement learning algorithms, which are separated into three main classes - value optimization, policy optimization, and imitation learning. Coach supports a large number of environments which can be solved using reinforcement learning.
