Showing 7 open source projects for "written in python"

View related business solutions
  • Keep company data safe with Chrome Enterprise Icon
    Keep company data safe with Chrome Enterprise

    Protect your business with AI policies and data loss prevention in the browser

    Make AI work your way with Chrome Enterprise. Block unapproved sites and set custom data controls that align with your company's policies.
    Download Chrome
  • Cloud-based help desk software with ServoDesk Icon
    Cloud-based help desk software with ServoDesk

    Full access to Enterprise features. No credit card required.

    What if You Could Automate 90% of Your Repetitive Tasks in Under 30 Days? At ServoDesk, we help businesses like yours automate operations with AI, allowing you to cut service times in half and increase productivity by 25% - without hiring more staff.
    Try ServoDesk for free
  • 1
    ConcurrentSim.jl

    ConcurrentSim.jl

    Discrete event process oriented simulation framework written in Julia

    A discrete event process-oriented simulation framework written in Julia inspired by the Python library SimPy. One of the longest-lived Julia packages (originally under the name SimJulia).
    Downloads: 0 This Week
    Last Update:
    See Project
  • 2
    UnROOT.jl

    UnROOT.jl

    Native Julia I/O package to work with CERN ROOT files objects

    UnROOT.jl is a reader for the CERN ROOT file format written entirely in Julia, without any dependence on ROOT or Python.
    Downloads: 0 This Week
    Last Update:
    See Project
  • 3
    Sundials.jl

    Sundials.jl

    Julia interface to Sundials, including a nonlinear solver

    This is a suite for numerically solving differential equations written in Julia and available for use in Julia, Python, and R. The purpose of this package is to supply efficient Julia implementations of solvers for various differential equations.
    Downloads: 0 This Week
    Last Update:
    See Project
  • 4
    CounterfactualExplanations.jl

    CounterfactualExplanations.jl

    A package for Counterfactual Explanations and Algorithmic Recourse

    CounterfactualExplanations.jl is a package for generating Counterfactual Explanations (CE) and Algorithmic Recourse (AR) for black-box algorithms. Both CE and AR are related tools for explainable artificial intelligence (XAI). While the package is written purely in Julia, it can be used to explain machine learning algorithms developed and trained in other popular programming languages like Python and R. See below for a short introduction and other resources or dive straight into the docs.
    Downloads: 0 This Week
    Last Update:
    See Project
  • Our Free Plans just got better! | Auth0 Icon
    Our Free Plans just got better! | Auth0

    With up to 25k MAUs and unlimited Okta connections, our Free Plan lets you focus on what you do best—building great apps.

    You asked, we delivered! Auth0 is excited to expand our Free and Paid plans to include more options so you can focus on building, deploying, and scaling applications without having to worry about your security. Auth0 now, thank yourself later.
    Try free now
  • 5
    Bayesian Julia

    Bayesian Julia

    Bayesian Statistics using Julia and Turing

    Bayesian statistics is an approach to inferential statistics based on Bayes' theorem, where available knowledge about parameters in a statistical model is updated with the information in observed data. The background knowledge is expressed as a prior distribution and combined with observational data in the form of a likelihood function to determine the posterior distribution. The posterior can also be used for making predictions about future events. Bayesian statistics is a departure from...
    Downloads: 0 This Week
    Last Update:
    See Project
  • 6
    Kinetic.jl

    Kinetic.jl

    Universal modeling and simulation of fluid mechanics upon ML

    Kinetic is a computational fluid dynamics toolbox written in Julia. It aims to furnish efficient modeling and simulation methodologies for fluid dynamics, augmented by the power of machine learning. Based on differentiable programming, mechanical and neural network models are fused and solved in a unified framework. Simultaneous 1-3 dimensional numerical simulations can be performed on CPUs and GPUs.
    Downloads: 2 This Week
    Last Update:
    See Project
  • 7
    Indicators.jl

    Indicators.jl

    Financial market technical analysis & indicators in Julia

    Indicators is a Julia package offering efficient implementations of many technical analysis indicators and algorithms. This work is inspired by the TTR package in R and the Python implementation of TA-Lib, and the ultimate goal is to implement all of the functionality of these offerings (and more) in Julia. This package has been written to be able to interface with both native Julia Array types, as well as the TS time series type from the Temporal package. Contributions are of course always welcome for wrapping any of these functions in methods for other types and/or packages out there, as are suggestions for other indicators to add to the lists.
    Downloads: 0 This Week
    Last Update:
    See Project
  • Previous
  • You're on page 1
  • Next