Showing 29 open source projects for "python-4suite-xml"

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  • 1
    Quadratic

    Quadratic

    Data science spreadsheet with Python & SQL

    Quadratic enables your team to work together on data analysis to deliver better results, faster. You already know how to use a spreadsheet, but you’ve never had this much power before. Quadratic is a Web-based spreadsheet application that runs in the browser and as a native app (via Electron). Our goal is to build a spreadsheet that enables you to pull your data from its source (SaaS, Database, CSV, API, etc) and then work with that data using the most popular data science tools today (Python...
    Downloads: 8 This Week
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  • 2
    DearPyGui

    DearPyGui

    Graphical User Interface Toolkit for Python with minimal dependencies

    Dear PyGui is an easy-to-use, dynamic, GPU-Accelerated, cross-platform graphical user interface toolkit(GUI) for Python. It is “built with” Dear ImGui. Features include traditional GUI elements such as buttons, radio buttons, menus, and various methods to create a functional layout. Additionally, DPG has an incredible assortment of dynamic plots, tables, drawings, debuggers, and multiple resource viewers. DPG is well suited for creating simple user interfaces as well as developing complex...
    Downloads: 8 This Week
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  • 3
    XGBoost

    XGBoost

    Scalable and Flexible Gradient Boosting

    ... can be used for Python, Java, Scala, R, C++ and more. It can run on a single machine, Hadoop, Spark, Dask, Flink and most other distributed environments, and is capable of solving problems beyond billions of examples.
    Downloads: 3 This Week
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  • 4
    marimo

    marimo

    A reactive notebook for Python

    marimo is an open-source reactive notebook for Python, reproducible, git-friendly, executable as a script, and shareable as an app. marimo notebooks are reproducible, extremely interactive, designed for collaboration (git-friendly!), deployable as scripts or apps, and fit for modern Pythonista. Run one cell and marimo reacts by automatically running affected cells, eliminating the error-prone chore of managing the notebook state. marimo's reactive UI elements, like data frame GUIs and plots...
    Downloads: 1 This Week
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  • 5
    Orchest

    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. Easily install language or system packages. Built on top of regular Docker container images...
    Downloads: 0 This Week
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  • 6
    NannyML

    NannyML

    Detecting silent model failure. NannyML estimates performance

    NannyML is an open-source python library that allows you to estimate post-deployment model performance (without access to targets), detect data drift, and intelligently link data drift alerts back to changes in model performance. Built for data scientists, NannyML has an easy-to-use interface, and interactive visualizations, is completely model-agnostic, and currently supports all tabular classification use cases. NannyML closes the loop with performance monitoring and post deployment data...
    Downloads: 0 This Week
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  • 7
    ClearML

    ClearML

    Streamline your ML workflow

    ClearML is an open source platform that automates and simplifies developing and managing machine learning solutions for thousands of data science teams all over the world. It is designed as an end-to-end MLOps suite allowing you to focus on developing your ML code & automation, while ClearML ensures your work is reproducible and scalable. The ClearML Python Package for integrating ClearML into your existing scripts by adding just two lines of code, and optionally extending your experiments...
    Downloads: 0 This Week
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  • 8
    PySyft

    PySyft

    Data science on data without acquiring a copy

    Most software libraries let you compute over the information you own and see inside of machines you control. However, this means that you cannot compute on information without first obtaining (at least partial) ownership of that information. It also means that you cannot compute using machines without first obtaining control over those machines. This is very limiting to human collaboration and systematically drives the centralization of data, because you cannot work with a bunch of data...
    Downloads: 0 This Week
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  • 9
    Metaflow

    Metaflow

    A framework for real-life data science

    Metaflow is a human-friendly Python library that helps scientists and engineers build and manage real-life data science projects. Metaflow was originally developed at Netflix to boost productivity of data scientists who work on a wide variety of projects from classical statistics to state-of-the-art deep learning.
    Downloads: 0 This Week
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  • 10
    NVIDIA Merlin

    NVIDIA Merlin

    Library providing end-to-end GPU-accelerated recommender systems

    NVIDIA Merlin is an open-source library that accelerates recommender systems on NVIDIA GPUs. The library enables data scientists, machine learning engineers, and researchers to build high-performing recommenders at scale. Merlin includes tools to address common feature engineering, training, and inference challenges. Each stage of the Merlin pipeline is optimized to support hundreds of terabytes of data, which is all accessible through easy-to-use APIs. For more information, see NVIDIA...
    Downloads: 0 This Week
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  • 11
    SageMaker Inference Toolkit

    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...
    Downloads: 0 This Week
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  • 12
    SageMaker Training Toolkit

    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...
    Downloads: 0 This Week
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  • 13
    Great Expectations

    Great Expectations

    Always know what to expect from your data

    Great Expectations helps data teams eliminate pipeline debt, through data testing, documentation, and profiling. Software developers have long known that testing and documentation are essential for managing complex codebases. Great Expectations brings the same confidence, integrity, and acceleration to data science and data engineering teams. Expectations are assertions for data. They are the workhorse abstraction in Great Expectations, covering all kinds of common data issues. Expectations...
    Downloads: 0 This Week
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  • 14
    TensorFlow.NET

    TensorFlow.NET

    .NET Standard bindings for Google's TensorFlow for developing models

    ... 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.
    Downloads: 0 This Week
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  • 15
    Cookiecutter Data Science

    Cookiecutter Data Science

    Project structure for doing and sharing data science work

    A logical, reasonably standardized, but flexible project structure for doing and sharing data science work. When we think about data analysis, we often think just about the resulting reports, insights, or visualizations. While these end products are generally the main event, it's easy to focus on making the products look nice and ignore the quality of the code that generates them. Because these end products are created programmatically, code quality is still important! And we're not talking...
    Downloads: 0 This Week
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  • 16
    tsfresh

    tsfresh

    Automatic extraction of relevant features from time series

    tsfresh is a python package. It automatically calculates a large number of time series characteristics, the so called features. tsfresh is used to to extract characteristics from time series. Without tsfresh, you would have to calculate all characteristics by hand. With tsfresh this process is automated and all your features can be calculated automatically. Further tsfresh is compatible with pythons pandas and scikit-learn APIs, two important packages for Data Science endeavours in python...
    Downloads: 0 This Week
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  • 17
    Recommenders

    Recommenders

    Best practices on recommendation systems

    The Recommenders repository provides examples and best practices for building recommendation systems, provided as Jupyter notebooks. The module reco_utils contains functions to simplify common tasks used when developing and evaluating recommender systems. Several utilities are provided in reco_utils to support common tasks such as loading datasets in the format expected by different algorithms, evaluating model outputs, and splitting training/test data. Implementations of several...
    Downloads: 0 This Week
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  • 18
    Synapse Machine Learning

    Synapse Machine Learning

    Simple and distributed Machine Learning

    SynapseML (previously MMLSpark) is an open source library to simplify the creation of scalable machine learning pipelines. 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,...
    Downloads: 0 This Week
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  • 19
    SADSA Advanced

    SADSA Advanced

    The SADSA is an integrated, user-friendly desktop solution

    The SADSA (Advanced) is a user-friendly, menu-driven Python application designed to perform a variety of statistical and analytical functions without requiring users to write any code. Built using Python, the app is tailored for students, researchers, and professionals seeking an interactive way to analyze datasets and visualize outcomes. It allows users to load datasets and conduct statistical analyses, view results and plots in dynamic output windows, and save reports in text and image...
    Downloads: 1 This Week
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  • 20
    Awesome Fraud Detection Research Papers

    Awesome Fraud Detection Research Papers

    A curated list of data mining papers about fraud detection

    A curated list of data mining papers about fraud detection from several conferences.
    Downloads: 0 This Week
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  • 21
    ML workspace

    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)...
    Downloads: 1 This Week
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  • 22
    Amazon SageMaker Examples

    Amazon SageMaker Examples

    Jupyter notebooks that demonstrate how to build models using SageMaker

    ... 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. They have the familiar Jupyter and JuypterLab interfaces that work well for single users, or small teams where users are also administrators. Advanced users also use SageMaker solely with the AWS CLI and Python scripts using boto3 and/or the SageMaker Python SDK.
    Downloads: 1 This Week
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  • 23
    Forecasting Best Practices

    Forecasting Best Practices

    Time Series Forecasting Best Practices & Examples

    ... 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.
    Downloads: 0 This Week
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  • 24

    DEPRECATED - KVFinder

    Cavity Detection PyMOL plugin

    ....2020.100606). [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).
    Downloads: 0 This Week
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  • 25
    SageMaker Containers

    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. The SageMaker Training Toolkit can be easily added to...
    Downloads: 0 This Week
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