¿Cuáles son las características principales de rstudio/cheatsheets?

Las características principales de rstudio/cheatsheets son: Printable Reference Sheets, Language-Specific Reference Sheets, Reference Cheatsheets, Community Translation Contributions, Multi-Format Document Rendering, Quarto-Powered Generations, Quarto-Powered Sites, Per-Project Library Isolations.

¿Qué alternativas de código abierto existen para rstudio/cheatsheets?

Las alternativas de código abierto para rstudio/cheatsheets incluyen: quarto-dev/quarto-cli — Quarto is an open-source scientific and technical publishing system built on Pandoc that converts Markdown and Jupyter… dmlc/mxnet — MXNet is a deep learning framework and distributed machine learning engine designed for training and deploying neural… fengwang/ceras — Ceras is yet another tiny deep learning engine, in pure c++ and header only. bvlc/caffe — Caffe is a high-performance deep learning framework designed for training and deploying deep neural networks. It… aria42/flare — Dynamic Tensor Graph library in Clojure (think PyTorch, DynNet, etc.). christoschristofidis/awesome-deep-learning — This project is a curated directory of resources, libraries, and frameworks designed to support the development,…

Cheatsheets - This repository is the source

Name: rstudio/cheatsheets
Author: rstudio

This repository is the source for a curated collection of printable reference sheets for R and Python packages. It provides quick-reference guides organized by topic, available as both PDF files for offline printing and interactive HTML versions for online browsing. The collection also includes community-contributed translations of these reference sheets into multiple languages, expanding accessibility for non-English speakers.

The project is built around a reproducible rendering pipeline that generates both PDF and HTML formats from source documents, using a lockfile to guarantee identical build environments across different machines. It manages contributions through a Git-based workflow with automated checks for HTML accessibility standards, and organizes cheatsheets by language variants using a directory-based naming convention. The entire collection can be rendered as a static Quarto website with managed dependencies and per-project package isolation.

Features

Printable Reference Sheets - Provides searchable printable PDF or HTML references for R and Python packages organized by topic.
Language-Specific Reference Sheets - Serves as the central curated collection of printable PDF and HTML reference sheets for R and Python packages.
Reference Cheatsheets - Offers interactive HTML versions of reference sheets for online browsing without downloading.
Community Translation Contributions - Provides community-contributed translations of reference sheets into multiple languages.
Multi-Format Document Rendering - Generates both PDF and HTML versions from source documents using a unified rendering pipeline.
Quarto-Powered Generations - Renders the cheatsheet collection as a static website using Quarto's document conversion pipeline.
Quarto-Powered Sites - Builds and deploys the cheatsheet collection as a Quarto website with reproducible rendering and dependency management.
Per-Project Library Isolations - Creates per-project package libraries to prevent dependency conflicts between projects.
Quick-Reference Guides - Provides printable quick-reference guides summarizing key functions for popular Python packages.
Build Reproducibility Lockfiles - Uses lockfiles to pin exact package versions for reproducible rendering across environments.
Exact Version Pinning - Snapshots exact package versions and sources into a lockfile for reproducible builds.
PDF Downloads - Provides printable PDF versions of each reference sheet for offline use and distribution.
Quick-Reference Guides - Provides printable quick-reference guides summarizing key functions for popular R packages.
Reference Sheets - Provides printable PDF or HTML reference sheets for R and Python packages organized by topic.
Contribution Workflows - Manages community contributions through pull requests with automated validation checks.
Project Isolation Managers - Creates private per-project libraries so packages installed for one project do not affect others.
Reproducible Build Sources - Provides source files and configuration to render the cheatsheet collection as a reproducible Quarto website.
Accessible HTML Contributions - Accepts community pull requests for accessible, text-based HTML cheatsheets with alt text and no images.
Community-Translated Documentation - Provides community-contributed translations of reference sheets in multiple languages.
Accessible HTML Contributions - Accepts community pull requests for accessible, text-based HTML cheatsheets with alt text and no images.
Directory-Based Locale Organizations - Organizes cheatsheets by language variants using directory-based naming conventions.
Deep Learning - Reference guides for deep learning architectures and model training.
Machine Learning - Cheat sheets for machine learning workflows and model implementation.
Data Science Cheat Sheets - Official collection of reference guides for data science tools.
Big Data - Reference guides for big data integration within R environments.
Data Visualization - Cheat sheets for building visualizations in R and Python.
General Data Science - General reference guides for data science tools and productivity.
R Programming - Official reference sheets for R packages and data science workflows.

quarto-dev/quarto-cli

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Quarto is an open-source scientific and technical publishing system built on Pandoc that converts Markdown and Jupyter notebooks into a wide range of output formats. It functions as a multi-format document converter, a reproducible research platform, a static site generator for technical content, and an interactive dashboard builder, all within a single framework. The system is distinguished by its ability to produce HTML, PDF, Word, ePub, and slide decks from a single Markdown source, while embedding executable code blocks in Python, R, Julia, or Observable for dynamic, reproducible document

dmlc/mxnet

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MXNet is a deep learning framework and distributed machine learning engine designed for training and deploying neural networks. It functions as a hardware-agnostic backend that allows for the development of deep learning models through a hybrid of symbolic and imperative programming. The system distinguishes itself through automatic distributed parallelism, which scales training workloads across multiple GPUs and machines. It features an extensible hardware backend interface that enables the integration of custom accelerators and proprietary libraries without modifying the core source code.

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Dynamic Tensor Graph library in Clojure (think PyTorch, DynNet, etc.)

Historial de estrellas

Dynamic Tensor Graph library in Clojure (think PyTorch, DynNet, etc.)

BVLC/caffe

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Caffe is a high-performance deep learning framework designed for training and deploying deep neural networks. It functions as a machine learning engine and a convolutional neural network library, providing a C++ backend to accelerate computations on both GPUs and CPUs. The system includes a specialized toolset for computer vision, enabling tasks such as object detection, semantic segmentation, and large-scale image retrieval. It supports the deployment of pre-trained models for image and scene recognition, as well as the ability to fine-tune neural network weights for specialized tasks. The

rstudiocheatsheets

Cheatsheets

Features