Search Results for "/storage/emulated/0/android/data/net.sourceforge.uiq3.fx603p/files" - Page 2
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Showing 58 open source projects for "/storage/emulated/0/android/data/net.sourceforge.uiq3.fx603p/files"
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anny is an all-in-one platform for managing hybrid workplaces and shared resources.Enable your employees to easily book desks, meeting rooms, parking spots, equipment, and more – all in one place. With flexible rules and group permissions, you stay in full control of who can access what.
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Effortlessly Manage Product InformationA single source of truth for all of your product information with easy ways to distribute that data to wherever it needs to go, including the most powerful e-commerce connectors in the industry.
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Synapse Machine Learning
Simple and distributed Machine Learning
...SynapseML builds on Apache Spark and SparkML to enable new kinds of machine learning, analytics, and model deployment workflows. SynapseML adds many deep learning and data science tools to the Spark ecosystem, including seamless integration of Spark Machine Learning pipelines with the Open Neural Network Exchange (ONNX), LightGBM, The Cognitive Services, Vowpal Wabbit, and OpenCV. These tools enable powerful and highly-scalable predictive and analytical models for a variety of data sources. SynapseML also brings new networking capabilities to the Spark Ecosystem. ... -
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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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MCPower
MCPower — simple Monte Carlo power analysis for complex models
MCPower-GUI is a desktop application that provides a graphical interface for the MCPower Monte Carlo power analysis library. It guides users through the full workflow across three tabs: Model setup (formula input with live parsing, CSV data upload with auto-detected variable types, effect size sliders, and correlation editing), Analysis configuration (find power for a given sample size or find the minimum sample size for a target power, with multiple testing correction and scenario analysis), and Results (interactive charts, exportable tables, and auto-generated Python replication scripts). ... -
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FlexiList.
FlexiList is a Java data structure that combines the benefits of array
FlexiList is a Java data structure that combines the benefits of arrays and linked lists. Like an array, it allows for efficient access to elements by index. Like a linked list, it allows for efficient insertion and deletion of elements at any position in the list. Benefits Over Arrays and ArrayList ->Efficient Insertion and Deletion: FlexiList can insert or delete nodes at any position in the list in O(1) time, whereas arrays require shifting all elements after the insertion or deletion point. ... -
Managed Cybersecurity Platform Built for MSPsIn a world that lives and breathes all things digital, every business is at risk. Cybersecurity has become a major problem for small and growing businesses due to limited budgets, resources, time, and training. Hackers are leveraging these vulnerabilities, and most of the existing cybersecurity solutions on the market are too cumbersome, too complicated, and far too costly.
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AWA-Core
Full application for factory, process engineer and Automation..
...More than an Historian... AWA-Core (Another Way of Automation) is a complete suite that allows engineers, PLC programmers and factory designers to create huge projects for retrieving data, creating graphics, automatic scripts, exports and data links. -
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TensorFlow.NET
.NET Standard bindings for Google's TensorFlow for developing models
...It aims to implement the complete Tensorflow API in C# which allows .NET developers to develop, train and deploy Machine Learning models with the cross-platform .NET Standard framework. TensorFlow.NET has built-in Keras high-level interface and is released as an independent package TensorFlow.Keras. SciSharp STACK's mission is to bring popular data science technology into the .NET world and to provide .NET developers with a powerful Machine Learning tool set without reinventing the wheel. Since the APIs are kept as similar as possible you can immediately adapt any existing TensorFlow code in C# or F# with a zero learning curve. Take a look at a comparison picture and see how comfortably a TensorFlow/Python script translates into a C# program with TensorFlow.NET. -
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SageMaker Inference Toolkit
Serve machine learning models within a Docker container
Serve 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. Once you have a trained model, you can include it in a Docker container that runs your inference code. A container provides an effectively isolated environment, ensuring a consistent runtime regardless of where the container is deployed. ... -
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Orchest
Build data pipelines, the easy way
Code, run and monitor your data pipelines all from your browser! From idea to scheduled pipeline in hours, not days. Interactively build your data science pipelines in our visual pipeline editor. Versioned as a JSON file. Run scripts or Jupyter notebooks as steps in a pipeline. Python, R, Julia, JavaScript, and Bash are supported. Parameterize your pipelines and run them periodically on a cron schedule. -
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CSAPP-Labs
Solutions and Notes for Labs of Computer Systems
CSAPP-Labs is a repository that organizes and provides practical lab exercises corresponding to the famous textbook Computer Systems: A Programmer’s Perspective (CS:APP), helping students deepen their understanding of how computer systems work at the machine level. The exercises cover core topics such as data representation, assembly language, processor architecture, cache behavior, memory hierarchy, linking, and concurrency, contextualizing abstract concepts from the book in real code and experiments. Each lab is structured to include test programs, Makefiles, harnesses, and step-by-step instructions that guide students through hands-on interaction with low-level programming and system behavior. ... -
Ditto Edge Server is a lightweight standalone server for resource-constrained edge environments, based on the core Ditto Edge SDK.Ditto's Edge SDK is the only thing your edge devices need to ensure your application is operational in any environment, regardless of network conditions.
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Self-learning-Computer-Science
Resources to learn computer science in your spare time
Self-learning Computer Science is a curated, open-source guide repository designed to help learners independently study computer science topics using high-quality university-level resources. The author (an undergraduate CS student) assembled links to courses from institutions like MIT, UC Berkeley, Stanford, etc., covering mathematics, programming, data structures/algorithms, computer architecture, machine learning, software engineering and more. It’s aimed at learners who find traditional course structures restrictive and want a flexible, self-paced path through CS, with a focus on building depth and breadth rather than shortcut exam skills. The repository provides a roadmap, references, teaching materials, and sometimes the author’s own project examples, offering both guidance and community support. ... -
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gophernotes
The Go kernel for Jupyter notebooks and nteract
...Use gophernotes to create and share documents that contain live Go code, equations, visualizations and explanatory text. These notebooks, with the live Go code, can then be shared with others via email, Dropbox, GitHub and the Jupyter Notebook Viewer. Go forth and do data science, or anything else interesting, with Go notebooks! This project utilizes a Go interpreter called gomacro under the hood to evaluate Go code interactively. The gophernotes logo was designed by the brilliant Marcus Olsson and was inspired by Renee French's original Go Gopher design. If you have the JUPYTER_PATH environmental variable set or if you are using an older version of Jupyter, you may need to copy this kernel config to another directory. -
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Deep Learning course
Slides and Jupyter notebooks for the Deep Learning lectures
Slides and Jupyter notebooks for the Deep Learning lectures at Master Year 2 Data Science from Institut Polytechnique de Paris. This course is being taught at as part of Master Year 2 Data Science IP-Paris. Note: press "P" to display the presenter's notes that include some comments and additional references. This lecture is built and maintained by Olivier Grisel and Charles Ollion. -
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ML workspace
All-in-one web-based IDE specialized for machine learning
All-in-one web-based development environment for machine learning. The ML workspace is an all-in-one web-based IDE specialized for machine learning and data science. It is simple to deploy and gets you started within minutes to productively built ML solutions on your own machines. This workspace is the ultimate tool for developers preloaded with a variety of popular data science libraries (e.g., Tensorflow, PyTorch, Keras, Sklearn) and dev tools (e.g., Jupyter, VS Code, Tensorboard) perfectly configured, optimized, and integrated. ... -
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Data Science Notes
Curated collection of data science learning materials
Data Science Notes is a large, curated collection of data science learning materials, with explanations, code snippets, and structured notes across the typical end-to-end workflow. It spans foundational math and statistics through data wrangling, visualization, machine learning, and practical project organization. The content emphasizes hands-on understanding by pairing narrative notes with runnable examples, making it useful for both self-study and classroom settings. ... -
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Power Analytics for Excel
Data Science made available for Excel
This plugin for Excel comes with statistical methods such as Muliple Regression, Analysis of Variance (ANOVA) and Distribution Tests. -
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DEPRECATED - KVFinder
Cavity Detection PyMOL plugin
...[pyKVFinder] pyKVFinder is available in this Python Package Index (PyPI) repository, https://pypi.org/project/pyKVFinder and this GitHub repository, https://github.com/LBC-LNBio/pyKVFinder. Please read and cite the original paper pyKVFinder: an efficient and integrable Python package for biomolecular cavity detection and characterization in data science (10.1186/s12859-021-04519-4). -
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Amazon SageMaker Examples
Jupyter notebooks that demonstrate how to build models using SageMaker
...If you’re new to SageMaker we recommend starting with more feature-rich SageMaker Studio. It uses the familiar JupyterLab interface and has seamless integration with a variety of deep learning and data science environments and scalable compute resources for training, inference, and other ML operations. Studio offers teams and companies easy on-boarding for their team members, freeing them up from complex systems admin and security processes. Administrators control data access and resource provisioning for their users. Notebook Instances are another option. ... -
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Forecasting Best Practices
Time Series Forecasting Best Practices & Examples
...Rather than creating implementations from scratch, we draw from existing state-of-the-art libraries and build additional utilities around processing and featuring the data, optimizing and evaluating models, and scaling up to the cloud. The examples and best practices are provided as Python Jupyter notebooks and R markdown files and a library of utility functions. -
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Data Science at the Command Line
Data science at the command line
Command Line by Jeroen Janssens, published by O’Reilly Media in October 2021. Obtain, scrub, explore, and model data with Unix Power Tools. This repository contains the full text, data, and scripts used in the second edition of the book Data Science at the Command Line by Jeroen Janssens. This thoroughly revised guide demonstrates how the flexibility of the command line can help you become a more efficient and productive data scientist. You’ll learn how to combine small yet powerful command-line tools to quickly obtain, scrub, explore, and model your data. ... -
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SageMaker Containers
Create SageMaker-compatible Docker containers
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 reliable training process. -
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TensorWatch
Debugging, monitoring and visualization for Python Machine Learning
...It enables developers to observe training behavior in real time through interactive visualizations, primarily within Jupyter Notebook environments. The tool treats most data interactions as streams, allowing flexible routing, storage, and visualization of metrics generated during model training. A distinctive capability is its “lazy logging” mode, which lets users query live training processes without pre-instrumenting all metrics ahead of time. TensorWatch supports multiple chart types and can be extended with custom visualizers and dashboards, making it highly adaptable for research workflows. ... -
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Deep Learning with PyTorch
Latest techniques in deep learning and representation learning
This course concerns the latest techniques in deep learning and representation learning, focusing on supervised and unsupervised deep learning, embedding methods, metric learning, convolutional and recurrent nets, with applications to computer vision, natural language understanding, and speech recognition. The prerequisites include DS-GA 1001 Intro to Data Science or a graduate-level machine learning course. To be able to follow the exercises, you are going to need a laptop with Miniconda (a minimal version of Anaconda) and several Python packages installed. The following instruction would work as is for Mac or Ubuntu Linux users, Windows users would need to install and work in the Git BASH terminal. ... -
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OGLDataScienceTool
Opengl tool for data science visualization
Data visualization tool written in LWJGL Compatible with libgdx and other opengl wrappers The project depends on apache poi, and apache commons, for office files support Planned features for next release: * reading json, and other nosql data structures * jdbc connection for creating dataframes * data heatmaps, and additional plots for questions, contact me kumar.santhi1982@hotmail.com more details: http://www.java-gaming.org/topics/ds/41920/view.html http://datascienceforindia.com/ -
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xsv
A fast CSV command line toolkit written in Rust
xsv is a command line program for indexing, slicing, analyzing, splitting and joining CSV files. Commands should be simple, fast and composable. Simple tasks should be easy. Performance trade offs should be exposed in the CLI interface. Composition should not come at the expense of performance. Let's say you're playing with some of the data from the Data Science Toolkit, which contains several CSV files. Maybe you're interested in the population counts of each city in the world. ...
