Search Results for "virtual-machine"
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Showing 25 open source projects for "virtual-machine"
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Go from Code to Production URL in SecondsSkip the Kubernetes configs. Cloud Run handles HTTPS, scaling, and infrastructure automatically. Two million requests free per month.
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Full-stack observability with actually useful AI | Grafana CloudBuilt on open standards like Prometheus and OpenTelemetry, Grafana Cloud includes Kubernetes Monitoring, Application Observability, Incident Response, plus the AI-powered Grafana Assistant. Get started with our generous free tier today.
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scikit-learn
Machine learning in Python
scikit-learn is an open source Python module for machine learning built on NumPy, SciPy and matplotlib. It offers simple and efficient tools for predictive data analysis and is reusable in various contexts. -
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dlib
Toolkit for making machine learning and data analysis applications
Dlib is a modern C++ toolkit containing machine learning algorithms and tools for creating complex software in C++ to solve real world problems. It is used in both industry and academia in a wide range of domains including robotics, embedded devices, mobile phones, and large high performance computing environments. Dlib's open source licensing allows you to use it in any application, free of charge. -
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.NET for Apache Spark
A free, open-source, and cross-platform big data analytics framework
.NET for Apache Spark provides high-performance APIs for using Apache Spark from C# and F#. With these .NET APIs, you can access the most popular Dataframe and SparkSQL aspects of Apache Spark, for working with structured data, and Spark Structured Streaming, for working with streaming data. .NET for Apache Spark is compliant with .NET Standard - a formal specification of .NET APIs that are common across .NET implementations. This means you can use .NET for Apache Spark anywhere you write... -
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Orange Data Mining
Orange: Interactive data analysis
Open source machine learning and data visualization. Build data analysis workflows visually, with a large, diverse toolbox. Perform simple data analysis with clever data visualization. Explore statistical distributions, box plots and scatter plots, or dive deeper with decision trees, hierarchical clustering, heatmaps, MDS and linear projections. Even your multidimensional data can become sensible in 2D, especially with clever attribute ranking and selections. -
Fully Managed MySQL, PostgreSQL, and SQL ServerCloud SQL handles your database ops end to end, so you can focus on your app.
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Elasticsearch
A Distributed RESTful Search Engine
Elasticsearch is a distributed, RESTful search and analytics engine that lets you store, search and analyze with ease at scale. It lets you perform and combine many types of searches; it scales seamlessly, and offers answers incredibly fast with search results you can rank based on a variety of factors. Elasticsearch can be used for a wide variety of use cases, from maps and metrics to site search and workplace search, and with all data types. -
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Pandas Profiling
Create HTML profiling reports from pandas DataFrame objects
pandas-profiling generates profile reports from a pandas DataFrame. The pandas df.describe() function is handy yet a little basic for exploratory data analysis. pandas-profiling extends pandas DataFrame with df.profile_report(), which automatically generates a standardized univariate and multivariate report for data understanding. High correlation warnings, based on different correlation metrics (Spearman, Pearson, Kendall, Cramér’s V, Phik). Most common categories (uppercase, lowercase,... -
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airda
airda(Air Data Agent
airda(Air Data Agent) is a multi-smart body for data analysis, capable of understanding data development and data analysis needs, understanding data, generating data-oriented queries, data visualization, machine learning and other tasks of SQL and Python codes. -
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IoTDB
Apache IoTDB
Apache IoTDB (Database for Internet of Things) is an IoT native database with high performance for data management and analysis, deployable on the edge and the cloud. Due to its light-weight architecture, high performance and rich feature set together with its deep integration with Apache Hadoop, Spark and Flink, Apache IoTDB can meet the requirements of massive data storage, high-speed data ingestion and complex data analysis in the IoT industrial fields. In the scene of factories, there... -
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POCO
Cross-platform C++ libraries for building network applications
The POCO C++ Libraries are powerful cross-platform C++ libraries for building network- and internet-based applications that run on desktop, server, mobile, IoT, and embedded systems. Whether building automation systems, industrial automation, IoT platforms, air traffic management systems, enterprise IT application and infrastructure management, security and network analytics, automotive infotainment and telematics, financial or healthcare, C++ developers have been trusting the POCO C++... -
MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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SageMaker Spark Container
Docker image used to run data processing workloads
...It provides high-level APIs in Scala, Java, Python, and R, and an optimized engine that supports general computation graphs for data analysis. It also supports a rich set of higher-level tools including Spark SQL for SQL and DataFrames, MLlib for machine learning, GraphX for graph processing, and Structured Streaming for stream processing. The SageMaker Spark Container is a Docker image used to run batch data processing workloads on Amazon SageMaker using the Apache Spark framework. The container images in this repository are used to build the pre-built container images that are used when running Spark jobs on Amazon SageMaker using the SageMaker Python SDK. ... -
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Eventer
Rapid, unbiased, reproducible analysis of synaptic events
...The software combines deconvolution for detection, and variable length template matching approaches for screening out false positive events. Eventer also includes a machine learning-based approach allowing users to train a model to implement their ‘expert’ selection criteria across data sets without bias. Sharing models allows users to implement consistent analysis procedures. The software is coded in MATLAB, but has been compiled as standalone applications for Windows, Mac and Linux. Please visit the official Eventer website for more info https://eventerneuro.netlify.app/ While the paper is in preparation, please cite as; Winchester, G., Liu, S., Steele, O.G., Aziz, W. and Penn, A.C. (2020) Eventer. ... -
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sadsa
SADSA (Software Application for Data Science and Analytics)
SADSA (Software Application for Data Science and Analytics) is a Python-based desktop application designed to simplify statistical analysis, machine learning, and data visualization for students, researchers, and data professionals. Built using Python for the GUI, SADSA provides a menu-driven interface for handling datasets, applying transformations, running advanced statistical tests, machine learning algorithms, and generating insightful plots — all without writing code. -
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Uranie
Uranie is CEA's uncertainty analysis platform, based on ROOT
Uranie is a sensitivity and uncertainty analysis plateform based on the ROOT framework (http://root.cern.ch) . It is developed at CEA, the French Atomic Energy Commission (http://www.cea.fr). It provides various tools for: - data analysis - sampling - statistical modeling - optimisation - sensitivity analysis - uncertainty analysis - running code on high performance computers - etc. Thanks to ROOT, it is easily scriptable in CINT (c++ like syntax) and Python. Is is... -
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Parallel and Distributed Process System
OmniSim simulates parallel and distributed processing systems
Parallel and Distributed Process OmniSim Computational Neuroscience: Large-scale neural population dynamics, brain-inspired computing architectures, and neuro-symbolic AI systems 🧬 Scientific Overview PDP-OmniSim is an advanced computational framework for simulating parallel and distributed processing systems, with cutting-edge applications in computational neuroscience, distributed computing, and complex systems modeling. The framework provides researchers with robust tools for... -
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PyNanoLab
data analysis and Visualization with matplotlib
PyNanoLab contains a variety of tools to complete the data analysis, statistics, curve fitting, and basic machine learning application. Visualization in pynanolab is based on matplotlib. The setup tools is desinged to control and set-up all the details of the figure with a GUI. -
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Log4jScanner
A log4j vulnerability filesystem scanner and Go package
...Instead of probing networks, it walks directories and archives, including nested JARs, to find version fingerprints and risky classes associated with the Log4Shell family of issues. The focus on static analysis makes it suitable for container images, build artifacts, and offline systems where active scanning isn’t feasible. Clear, machine-readable output allows the tool to plug into CI/CD checks and fleet-wide inventory jobs. For responders, it reduces time-to-visibility by surfacing exactly which paths and bundles require patching or remediation. It’s a pragmatic addition to defense-in-depth programs that need verifiable evidence of exposure without deploying agents. -
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EZStacking
EZStacking is Jupyter notebook generator for machine learning
EZStacking is Jupyter notebook generator for supervised learning problems using Scikit-Learn pipelines and stacked generalization. EZStacking handles classification and regression problems for structured data. It can also be viewed as a development tool, because a notebook generated with EZStacking contains: -an exploratory data analysis (EDA) used to assess data quality - a modelling producing a reduced-size stacked estimator - a server returning a prediction, a measure of the quality... -
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AI learning
AiLearning, data analysis plus machine learning practice
We actively respond to the Research Open Source Initiative (DOCX) . Open source today is not just open source, but datasets, models, tutorials, and experimental records. We are also exploring other categories of open source solutions and protocols. I hope you will understand this initiative, combine this initiative with your own interests, and do what you can. Everyone's tiny contributions, together, are the entire open source ecosystem. We are iBooker, a large open-source community,... -
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Open Cezeri Library
Effective Linear Algebra and Computer Vision Library with JAVA
...To learn for library please try to use test examples at OpenCezeriLibrary\test\test. It is originally developed at el-cezeri laboratory of Siirt University, in order to establish generic framework of reusable components and software tools for machine vision, machine learning, AI and robotic applications. Currently, it holds following main concepts 1- Vision: It can access web cams, imaging source industrial cameras for manuel settings and advanced issues. Studies on accesing Leapmotion and Kinect is still under-development. 2- Machine learning: It uses Weka Software tool and some personel coded ML algorithms 3- CMatrix: Special matrix library called as CMatrix meaning Cezeri Maztrix Class. ... -
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Savuka-VirtualBox
Virtual Box VDI of SliTaz Linux with Savuka installed and configured
...root name: root root password: root username: tux password: root Instructions: -Download and install 7zip (www.7-zip.org) -Extract files to HDD -Install included VirtualBox (not tested on other versions, but shouldn't matter) -Within VirtualBox make a new machine ----Machine/New --------Name: Savuka / Type: Linux / Version: Other Linux (32-bit) --------1024MB RAM -------- Use Existing virtual drive (point to extracted Savuka.vdi) ---- Machine/Settigns/Display: set video memory to 50 MB, enable 2D and 3D acceleration ---- Machine/Settings/Shared Folders: Create new machine folder. ... -
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feed4weka is an open library that enriches weka (http://www.cs.waikato.ac.nz/ml/weka/), an open source project for data analysis. It integrates new classification and clustering algorithms, and adds the coclustering and outlier detection frameworks
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JProGraM (PRObabilistic GRAphical Models in Java) is a statistical machine learning library. It supports statistical modeling and data analysis along three main directions: (1) probabilistic graphical models (Bayesian networks, Markov random fields, dependency networks, hybrid random fields); (2) parametric, semiparametric, and nonparametric density estimation (Gaussian models, nonparanormal estimators, Parzen windows, Nadaraya-Watson estimator); (3) generative models for random networks (small-world, scale-free, exponential random graphs, Fiedler random fields), subgraph sampling algorithms (random walk, snowball, etc.), and spectral decomposition.
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Java OpenCL Process Virtual Machine. Spring IoC based framework for complex data analysis with OpenCL computing.
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