PredictionBuilder (Swift implementation) is an open-source machine learning library hosted on GitHub, designed to build predictions using linear regression. The repository provides documentation for installation, usage examples, and licensing information.
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PyBrain is a Python machine learning library distributed under a BSD-3-Clause license. The repository includes documentation, examples, and a setup script; installation requires SciPy and is performed with python setup.py install or by cloning the repository. The project provides a quickstart, tutorials, and an API reference in its documentation.
PyDexter is a Python wrapper for D3xter that renders charts in the browser using simple Python syntax. It supports histogram, scatter, plot, pie, timeline and bar chart examples. Installation is done with pip install PyDexter, and charts can be configured with settings such as height, width and title.
REP is a GitHub-hosted machine learning toolbox developed by Yandex, branded as a Reproducible Experiment Platform. It includes documentation for its main features, usage examples, and installation guidance for both Docker and manual setup.
A Python implementation of Restricted Boltzmann Machines. It lets users initialize an RBM with a chosen number of visible and hidden units, train it on a matrix of training examples, run visible or hidden data, and let the network run freely. It is released under an MIT license.
Examples showing how to use the OpenAI vision API to run inference on images, video files and webcam streams.
Open Source, Community Driven, Documentation for Techno Tim YouTube Videos/ Complete with examples for all your copy pasta needs!
tldr-pages is a collection of community-maintained help pages for command-line tools, presented as a simpler complement to traditional man pages. The repository holds examples for common UNIX, Linux, macOS, FreeBSD, NetBSD, OpenBSD, SunOS, Android, Windows, Cisco IOS and DOS command-line tools. Official clients are available for Python, Rust and Node.js.
torchvision is a package of popular datasets, model architectures and common image transformations for computer vision. The repository holds documentation, benchmarks, examples, packaging, references and tests alongside the torchvision code. Installation instructions point to official guidance for stable torch and torchvision versions; building from source is covered on the contributing page.