Showing 746 open source projects for "python-snap7"

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  • 1
    Lightly

    Lightly

    A python library for self-supervised learning on images

    ...This allows selecting the best core set of samples for model training through advanced filtering. We provide PyTorch, PyTorch Lightning and PyTorch Lightning distributed examples for each of the models to kickstart your project. Lightly requires Python 3.6+ but we recommend using Python 3.7+. We recommend installing Lightly in a Linux or OSX environment. With lightly, you can use the latest self-supervised learning methods in a modular way using the full power of PyTorch. Experiment with different backbones, models, and loss functions.
    Downloads: 0 This Week
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  • 2
    AmpliGraph

    AmpliGraph

    Python library for Representation Learning on Knowledge Graphs

    Open source library based on TensorFlow that predicts links between concepts in a knowledge graph. AmpliGraph is a suite of neural machine learning models for relational Learning, a branch of machine learning that deals with supervised learning on knowledge graphs.
    Downloads: 0 This Week
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  • 3
    Adversarial Robustness Toolbox

    Adversarial Robustness Toolbox

    Adversarial Robustness Toolbox (ART) - Python Library for ML security

    Adversarial Robustness Toolbox (ART) is a Python library for Machine Learning Security. ART provides tools that enable developers and researchers to evaluate, defend, certify and verify Machine Learning models and applications against the adversarial threats of Evasion, Poisoning, Extraction, and Inference. ART supports all popular machine learning frameworks (TensorFlow, Keras, PyTorch, MXNet, sci-kit-learn, XGBoost, LightGBM, CatBoost, GPy, etc.), all data types (images, tables, audio, video, etc.) and machine learning tasks (classification, object detection, generation, certification, etc.).
    Downloads: 0 This Week
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  • 4
    AlphaZero.jl

    AlphaZero.jl

    A generic, simple and fast implementation of Deepmind's AlphaZero

    ...Because AlphaZero is resource-hungry, successful open-source implementations (such as Leela Zero) are written in low-level languages (such as C++) and optimized for highly distributed computing environments. This makes them hardly accessible for students, researchers and hackers. Many simple Python implementations can be found on Github, but none of them is able to beat a reasonable baseline on games such as Othello or Connect Four. As an illustration, the benchmark in the README of the most popular of them only features a random baseline, along with a greedy baseline that does not appear to be significantly stronger.
    Downloads: 17 This Week
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  • 5
    Label Studio

    Label Studio

    Label Studio is a multi-type data labeling and annotation tool

    The most flexible data annotation tool. Quickly installable. Build custom UIs or use pre-built labeling templates. Detect objects on image, bboxes, polygons, circular, and keypoints supported. Partition image into multiple segments. Use ML models to pre-label and optimize the process. Label Studio is an open-source data labeling tool. It lets you label data types like audio, text, images, videos, and time series with a simple and straightforward UI and export to various model formats. It can...
    Downloads: 14 This Week
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  • 6
    StatsForecast

    StatsForecast

    Fast forecasting with statistical and econometric models

    ...Its internal implementation leverages numba to compile performance-critical code to optimized machine-level instructions, which makes the models much faster than many traditional Python counterparts.
    Downloads: 0 This Week
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  • 7
    OpenCV

    OpenCV

    Open Source Computer Vision Library

    ...It enables developers to build real-time vision applications ranging from facial recognition to object tracking. OpenCV supports a wide range of programming languages including C++, Python, and Java, and is optimized for both CPU and GPU operations.
    Downloads: 28 This Week
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  • 8
    Intel Extension for PyTorch

    Intel Extension for PyTorch

    A Python package for extending the official PyTorch

    Intel® Extension for PyTorch* extends PyTorch* with up-to-date features optimizations for an extra performance boost on Intel hardware. Optimizations take advantage of Intel® Advanced Vector Extensions 512 (Intel® AVX-512) Vector Neural Network Instructions (VNNI) and Intel® Advanced Matrix Extensions (Intel® AMX) on Intel CPUs as well as Intel Xe Matrix Extensions (XMX) AI engines on Intel discrete GPUs. Moreover, Intel® Extension for PyTorch* provides easy GPU acceleration for Intel...
    Downloads: 0 This Week
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  • 9
    Daft

    Daft

    Distributed DataFrame for Python designed for the cloud

    Daft is a framework for ETL, analytics and ML/AI at scale. Its familiar Python Dataframe API is built to outperform Spark in performance and ease of use. Daft plugs directly into your ML/AI stack through efficient zero-copy integrations with essential Python libraries such as Pytorch and Ray. It also allows requesting GPUs as a resource for running models. Daft runs locally with a lightweight multithreaded backend.
    Downloads: 0 This Week
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  • 10
    TorchRec

    TorchRec

    Pytorch domain library for recommendation systems

    TorchRec is a PyTorch domain library built to provide common sparsity & parallelism primitives needed for large-scale recommender systems (RecSys). It allows authors to train models with large embedding tables sharded across many GPUs. Parallelism primitives that enable easy authoring of large, performant multi-device/multi-node models using hybrid data-parallelism/model-parallelism. The TorchRec sharder can shard embedding tables with different sharding strategies including data-parallel,...
    Downloads: 1 This Week
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  • 11
    NGBoost

    NGBoost

    Natural Gradient Boosting for Probabilistic Prediction

    ngboost is a Python library that implements Natural Gradient Boosting, as described in "NGBoost: Natural Gradient Boosting for Probabilistic Prediction". It is built on top of Scikit-Learn and is designed to be scalable and modular with respect to the choice of proper scoring rule, distribution, and base learner. A didactic introduction to the methodology underlying NGBoost is available in this slide deck.
    Downloads: 0 This Week
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  • 12
    LibrePhotos

    LibrePhotos

    A self-hosted open source photo management service

    LibrePhotos is an open-source self-hosted photo management platform designed to organize, browse, and analyze personal media libraries while preserving user privacy. The system allows individuals to store and manage their photos and videos locally rather than relying on commercial cloud services. It provides features similar to services like Google Photos but runs on a private server controlled by the user. The application includes AI-powered tools that automatically analyze images to detect...
    Downloads: 8 This Week
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  • 13
    machine_learning_examples

    machine_learning_examples

    A collection of machine learning examples and tutorials

    ...The project aims to teach machine learning concepts through hands-on programming rather than purely theoretical explanations. It includes implementations of many machine learning algorithms and neural network architectures using Python and popular libraries such as TensorFlow and NumPy. The repository covers a wide range of topics including supervised learning, unsupervised learning, reinforcement learning, and natural language processing. Many of the examples are accompanied by tutorials and educational materials that explain how the algorithms work and how they can be applied in real-world projects. ...
    Downloads: 0 This Week
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  • 14
    Fairlearn

    Fairlearn

    A Python package to assess and improve fairness of ML models

    Fairlearn is a Python package that empowers developers of artificial intelligence (AI) systems to assess their system's fairness and mitigate any observed unfairness issues. Fairlearn contains mitigation algorithms as well as metrics for model assessment. Besides the source code, this repository also contains Jupyter notebooks with examples of Fairlearn usage.
    Downloads: 0 This Week
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  • 15
    Causal ML

    Causal ML

    Uplift modeling and causal inference with machine learning algorithms

    Causal ML is a Python package that provides a suite of uplift modeling and causal inference methods using machine learning algorithms based on recent research [1]. It provides a standard interface that allows users to estimate the Conditional Average Treatment Effect (CATE) or Individual Treatment Effect (ITE) from experimental or observational data.
    Downloads: 0 This Week
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  • 16
    Finance

    Finance

    150+ quantitative finance Python programs

    Finance is a repository that compiles structured notes and educational material related to financial analysis, markets, and quantitative finance concepts. The project focuses on explaining key principles used in finance and investment analysis, including topics such as financial statements, valuation models, portfolio theory, and financial markets. The repository is designed as a study reference for students and professionals who want to understand financial systems and the analytical...
    Downloads: 1 This Week
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  • 17
    Pedalboard

    Pedalboard

    A Python library for audio

    pedalboard is a Python library for working with audio: reading, writing, rendering, adding effects, and more. It supports the most popular audio file formats and a number of common audio effects out of the box and also allows the use of VST3® and Audio Unit formats for loading third-party software instruments and effects. pedalboard was built by Spotify’s Audio Intelligence Lab to enable using studio-quality audio effects from within Python and TensorFlow.
    Downloads: 0 This Week
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  • 18
    Darts

    Darts

    A python library for easy manipulation and forecasting of time series

    ...The ML-based models can be trained on potentially large datasets containing multiple time series, and some of the models offer a rich support for probabilistic forecasting. We recommend to first setup a clean Python environment for your project with at least Python 3.7 using your favorite tool (conda, venv, virtualenv with or without virtualenvwrapper).
    Downloads: 0 This Week
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  • 19
    KerasTuner

    KerasTuner

    A Hyperparameter Tuning Library for Keras

    KerasTuner is an easy-to-use, scalable hyperparameter optimization framework that solves the pain points of hyperparameter search. Easily configure your search space with a define-by-run syntax, then leverage one of the available search algorithms to find the best hyperparameter values for your models. KerasTuner comes with Bayesian Optimization, Hyperband, and Random Search algorithms built-in, and is also designed to be easy for researchers to extend in order to experiment with new search...
    Downloads: 0 This Week
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  • 20
    TensorFlow Datasets

    TensorFlow Datasets

    TFDS is a collection of datasets ready to use with TensorFlow,

    TensorFlow Datasets is a collection of datasets ready to use, with TensorFlow or other Python ML frameworks, such as Jax. All datasets are exposed as tf.data. Datasets , enabling easy-to-use and high-performance input pipelines. To get started see the guide and our list of datasets.
    Downloads: 0 This Week
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  • 21
    HivisionIDPhoto

    HivisionIDPhoto

    HivisionIDPhotos: a lightweight and efficient AI ID photos tools

    HivisionIDPhotos is an open-source AI project designed to automatically generate professional ID photographs from ordinary portrait images. The system uses computer vision and machine learning models to detect faces, segment the subject from the background, and produce standardized identification photos suitable for official documents. It is designed as a lightweight tool that can perform inference offline and run efficiently on CPUs without requiring powerful GPUs. The software analyzes...
    Downloads: 7 This Week
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  • 22
    Evaluate

    Evaluate

    A library for easily evaluating machine learning models and datasets

    Evaluate is a library that makes evaluating and comparing models and reporting their performance easier and more standardized.
    Downloads: 0 This Week
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  • 23
    Zero to Mastery Machine Learning

    Zero to Mastery Machine Learning

    All course materials for the Zero to Mastery Machine Learning

    Zero to Mastery Machine Learning is an open-source repository that contains the complete course materials for the Zero to Mastery Machine Learning and Data Science bootcamp. The project provides a structured curriculum designed to teach machine learning and data science using Python through hands-on projects and interactive notebooks. The repository includes datasets, Jupyter notebooks, documentation, and example code that walk learners through the entire machine learning workflow from problem definition to model deployment. The course introduces essential tools such as NumPy, pandas, Matplotlib, and scikit-learn before moving on to deep learning with frameworks like TensorFlow and Keras. ...
    Downloads: 5 This Week
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  • 24
    segment-geospatial

    segment-geospatial

    A Python package for segmenting geospatial data with the SAM

    The segment-geospatial package draws its inspiration from segment-anything-eo repository authored by Aliaksandr Hancharenka. To facilitate the use of the Segment Anything Model (SAM) for geospatial data, I have developed the segment-anything-py and segment-geospatial Python packages, which are now available on PyPI and conda-forge. My primary objective is to simplify the process of leveraging SAM for geospatial data analysis by enabling users to achieve this with minimal coding effort. I have adapted the source code of segment-geospatial from the segment-anything-eo repository, and credit for its original version goes to Aliaksandr Hancharenka.
    Downloads: 0 This Week
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  • 25
    MLJAR Studio

    MLJAR Studio

    Python package for AutoML on Tabular Data with Feature Engineering

    We are working on new way for visual programming. We developed a desktop application called MLJAR Studio. It is a notebook-based development environment with interactive code recipes and a managed Python environment. All running locally on your machine. We are waiting for your feedback. The mljar-supervised is an Automated Machine Learning Python package that works with tabular data. It is designed to save time for a data scientist. It abstracts the common way to preprocess the data, construct the machine learning models, and perform hyper-parameter tuning to find the best model. ...
    Downloads: 0 This Week
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