Search Results for "artificial intelligence programming assistent"
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Showing 8 open source projects for "artificial intelligence programming assistent"
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All-in-One Mental Health EHREnsora Health’s Mental Health EHR is designed for mental health professionals, therapists, and practice managers looking for a secure, user-friendly solution to streamline administrative tasks and improve efficiency in their practice management
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Enterprise-Class Managed File Transfer.Diplomat MFT by Coviant Software is a secure, reliable managed file transfer solution designed to simplify and automate SFTP, FTPS, and HTTPS file transfers. Built for seamless integration, Diplomat MFT works across major cloud storage platforms, including AWS S3, Azure Blob, Google Cloud, Oracle Cloud, SharePoint, Dropbox, Box, and more.
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The Julia Programming Language
High-level, high-performance dynamic language for technical computing
Julia is a fast, open source high-performance dynamic language for technical computing. It can be used for data visualization and plotting, deep learning, machine learning, scientific computing, parallel computing and so much more. Having a high level syntax, Julia is easy to use for programmers of every level and background. Julia has more than 2,800 community-registered packages including various mathematical libraries, data manipulation tools, and packages for general purpose... -
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Turing.jl
Bayesian inference with probabilistic programming
Bayesian inference with probabilistic programming. -
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Zygote
21st century AD
Zygote provides source-to-source automatic differentiation (AD) in Julia, and is the next-gen AD system for the Flux differentiable programming framework. For more details and benchmarks of Zygote's technique, see our paper. You may want to check out Flux for more interesting examples of Zygote usage; the documentation here focuses on internals and advanced AD usage. -
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Flux.jl
Relax! Flux is the ML library that doesn't make you tensor
Flux is an elegant approach to machine learning. It's a 100% pure Julia stack and provides lightweight abstractions on top of Julia's native GPU and AD support. Flux makes the easy things easy while remaining fully hackable. Flux provides a single, intuitive way to define models, just like mathematical notation. Julia transparently compiles your code, optimizing and fusing kernels for the GPU, for the best performance. Existing Julia libraries are differentiable and can be incorporated... -
Melis Platform is an enterprise-grade Low Code Platform simplifying app creation, management, and delivery.Melis is a new generation of Content Management System and eCommerce platform to achieve and manage websites from a single web interface easy to use while offering the best of open source technology.
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Gen.jl
A general-purpose probabilistic programming system
An open-source stack for generative modeling and probabilistic inference. Gen’s inference library gives users building blocks for writing efficient probabilistic inference algorithms that are tailored to their models, while automating the tricky math and the low-level implementation details. Gen helps users write hybrid algorithms that combine neural networks, variational inference, sequential Monte Carlo samplers, and Markov chain Monte Carlo. Gen features an easy-to-use modeling language... -
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AutoMLPipeline.jl
Package that makes it trivial to create and evaluate machine learning
AutoMLPipeline (AMLP) is a package that makes it trivial to create complex ML pipeline structures using simple expressions. It leverages on the built-in macro programming features of Julia to symbolically process, and manipulate pipeline expressions and makes it easy to discover optimal structures for machine learning regression and classification. To illustrate, here is a pipeline expression and evaluation of a typical machine learning workflow that extracts numerical features (numf) for... -
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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. -
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Pythonidae
Curated decibans of scientific programming resources in Python
Pythonidae is a curated collection of scientific programming resources in Python, designed to support research and development across a wide range of disciplines. The repository organizes tools and libraries into domain-specific categories, including mathematics, statistics, machine learning, artificial intelligence, biology, chemistry, physics, earth sciences, and supercomputing. It also covers practical areas such as build automation, databases, APIs, computer graphics, and utilities, offering a structured reference for both academic and applied work. ...
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