Showing 565 open source projects for "python linux"

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  • 1
    PyTensor

    PyTensor

    Python library for defining and optimizing mathematical expressions

    PyTensor is a fork of Aesara, a Python library for defining, optimizing, and efficiently evaluating mathematical expressions involving multi-dimensional arrays. PyTensor is based on Theano, which has been powering large-scale computationally intensive scientific investigations since 2007. A hackable, pure-Python codebase. Extensible graph framework is suitable for rapid development of custom operators and symbolic optimizations. Implements an extensible graph transpilation framework that...
    Downloads: 2 This Week
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  • 2
    Lepton AI

    Lepton AI

    A Pythonic framework to simplify AI service building

    A Pythonic framework to simplify AI service building. Cutting-edge AI inference and training, unmatched cloud-native experience, and top-tier GPU infrastructure. Ensure 99.9% uptime with comprehensive health checks and automatic repairs.
    Downloads: 9 This Week
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  • 3
    EconML

    EconML

    Python Package for ML-Based Heterogeneous Treatment Effects Estimation

    EconML is a Python package for estimating heterogeneous treatment effects from observational data via machine learning. This package was designed and built as part of the ALICE project at Microsoft Research with the goal of combining state-of-the-art machine learning techniques with econometrics to bring automation to complex causal inference problems. One of the biggest promises of machine learning is to automate decision-making in a multitude of domains. At the core of many data-driven...
    Downloads: 4 This Week
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  • 4
    Advanced Solutions Lab

    Advanced Solutions Lab

    This repos contains notebooks for the Advanced Solutions Lab

    This repository contains Jupyter notebooks meant to be run on Vertex AI. This is maintained by Google Cloud’s Advanced Solutions Lab (ASL) team. Vertex AI is the next-generation AI Platform on the Google Cloud Platform. The material covered in this repo will take a software engineer with no exposure to machine learning to an advanced level.
    Downloads: 14 This Week
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  • 5
    Mlxtend

    Mlxtend

    A library of extension and helper modules for Python's data analysis

    Mlxtend (machine learning extensions) is a Python library of useful tools for day-to-day data science tasks.
    Downloads: 2 This Week
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  • 6
    SKORCH

    SKORCH

    A scikit-learn compatible neural network library that wraps PyTorch

    A scikit-learn compatible neural network library that wraps PyTorch.
    Downloads: 1 This Week
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  • 7
    PyBroker

    PyBroker

    Algorithmic Trading in Python with Machine Learning

    Are you looking to enhance your trading strategies with the power of Python and machine learning? Then you need to check out PyBroker! This Python framework is designed for developing algorithmic trading strategies, with a focus on strategies that use machine learning. With PyBroker, you can easily create and fine-tune trading rules, build powerful models, and gain valuable insights into your strategy’s performance.
    Downloads: 3 This Week
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  • 8
    TensorFlow

    TensorFlow

    TensorFlow is an open source library for machine learning

    Originally developed by Google for internal use, TensorFlow is an open source platform for machine learning. Available across all common operating systems (desktop, server and mobile), TensorFlow provides stable APIs for Python and C as well as APIs that are not guaranteed to be backwards compatible or are 3rd party for a variety of other languages. The platform can be easily deployed on multiple CPUs, GPUs and Google's proprietary chip, the tensor processing unit (TPU). TensorFlow...
    Downloads: 16 This Week
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  • 9
    huggingface_hub

    huggingface_hub

    The official Python client for the Huggingface Hub

    The huggingface_hub library allows you to interact with the Hugging Face Hub, a platform democratizing open-source Machine Learning for creators and collaborators. Discover pre-trained models and datasets for your projects or play with the thousands of machine-learning apps hosted on the Hub. You can also create and share your own models, datasets, and demos with the community. The huggingface_hub library provides a simple way to do all these things with Python.
    Downloads: 2 This Week
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  • 10
    APKiD

    APKiD

    Android Application Identifier for Packers, Protectors and Obfuscators

    APKiD gives you information about how an APK was made. It identifies many compilers, packers, obfuscators, and other weird stuff. It's PEiD for Android.
    Downloads: 8 This Week
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  • 11
    FLAML

    FLAML

    A fast library for AutoML and tuning

    FLAML is a lightweight Python library that finds accurate machine learning models automatically, efficiently and economically. It frees users from selecting learners and hyperparameters for each learner. For common machine learning tasks like classification and regression, it quickly finds quality models for user-provided data with low computational resources. It supports both classical machine learning models and deep neural networks. It is easy to customize or extend. Users can find their...
    Downloads: 11 This Week
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  • 12
    DoWhy

    DoWhy

    DoWhy is a Python library for causal inference

    DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks. Much like machine learning libraries have done for prediction, DoWhy is a Python library that aims to spark causal thinking and analysis. DoWhy provides a wide variety of algorithms for effect estimation, causal structure learning, diagnosis of causal...
    Downloads: 2 This Week
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  • 13
    Chemprop

    Chemprop

    Message Passing Neural Networks for Molecule Property Prediction

    Chemprop is a repository containing message-passing neural networks for molecular property prediction.
    Downloads: 4 This Week
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  • 14
    Shapash

    Shapash

    Explainability and Interpretability to Develop Reliable ML models

    Shapash is a Python library dedicated to the interpretability of Data Science models. It provides several types of visualization that display explicit labels that everyone can understand. Data Scientists can more easily understand their models, share their results and easily document their projects in an HTML report. End users can understand the suggestion proposed by a model using a summary of the most influential criteria.
    Downloads: 5 This Week
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  • 15
    dm_control

    dm_control

    DeepMind's software stack for physics-based simulation

    ...On Linux these can be installed using your distribution's package manager. "Headless" hardware rendering (i.e. without a windowing system such as X11) requires EXT_platform_device support in the EGL driver. While dm_control has been largely updated to use the pybind11-based bindings provided via the mujoco package, at this time it still relies on some legacy components that are automatically generated.
    Downloads: 8 This Week
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  • 16
    gensim

    gensim

    Topic Modelling for Humans

    Gensim is a Python library for topic modeling, document indexing, and similarity retrieval with large corpora. The target audience is the natural language processing (NLP) and information retrieval (IR) community.
    Downloads: 4 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. Internally at Spotify, pedalboard...
    Downloads: 4 This Week
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  • 18
    pycm

    pycm

    Multi-class confusion matrix library in Python

    PyCM is a multi-class confusion matrix library written in Python that supports both input data vectors and direct matrix, and a proper tool for post-classification model evaluation that supports most classes and overall statistics parameters. PyCM is the swiss-army knife of confusion matrices, targeted mainly at data scientists that need a broad array of metrics for predictive models and an accurate evaluation of large variety of classifiers.
    Downloads: 0 This Week
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  • 19
    TensorLy

    TensorLy

    Tensor Learning in Python

    TensorLy is a Python library that aims at making tensor learning simple and accessible. It allows to easily perform tensor decomposition, tensor learning and tensor algebra. Its backend system allows to seamlessly perform computation with NumPy, PyTorch, JAX, TensorFlow, CuPy or Paddle, and run methods at scale on CPU or GPU.
    Downloads: 0 This Week
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  • 20
    PennyLane

    PennyLane

    A cross-platform Python library for differentiable programming

    A cross-platform Python library for differentiable programming of quantum computers. Train a quantum computer the same way as a neural network. Built-in automatic differentiation of quantum circuits, using the near-term quantum devices directly. You can combine multiple quantum devices with classical processing arbitrarily! Support for hybrid quantum and classical models, and compatible with existing machine learning libraries. Quantum circuits can be set up to interface with either NumPy,...
    Downloads: 7 This Week
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  • 21
    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. An AI system can behave unfairly for a variety of reasons. In Fairlearn, we define whether an AI system is behaving unfairly in terms...
    Downloads: 3 This Week
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  • 22
    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. Essentially, it estimates the causal impact of intervention T on outcome Y for users with observed features X, without strong assumptions on...
    Downloads: 3 This Week
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  • 23
    gplearn

    gplearn

    Genetic Programming in Python, with a scikit-learn inspired API

    gplearn implements Genetic Programming in Python, with a scikit-learn-inspired and compatible API. While Genetic Programming (GP) can be used to perform a very wide variety of tasks, gplearn is purposefully constrained to solving symbolic regression problems. This is motivated by the scikit-learn ethos, of having powerful estimators that are straightforward to implement. Symbolic regression is a machine learning technique that aims to identify an underlying mathematical expression that best...
    Downloads: 2 This Week
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  • 24
    PyTextRank

    PyTextRank

    Python implementation of TextRank algorithms

    PyTextRank is a Python implementation of TextRank as a spaCy pipeline extension, for graph-based natural language work -- and related knowledge graph practices.
    Downloads: 0 This Week
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  • 25
    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: 2 This Week
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