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Google Open Source Project Style Guide
Chinese version of Google open source project style guide
Each larger open source project has its own style guide, a series of conventions on how to write code for the project (sometimes more arbitrary). When all the code maintains a consistent style, it is more important when understanding large code bases. easy. The meaning of "style" covers a wide range, from "variables use camelCase" to "never use global variables" to "never use exceptions". -
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Hello Python
Comprehensive tutorial repository aimed at teaching the Python program
...In addition, it is accompanied by a practical coding approach (projects) and is maintained as an open-source repository under Apache-2.0 license. It’s ideal for learners who want structured content, hands-on practice, and community guidance to build their Python skills. -
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Mastering Bitcoin
Mastering Bitcoin 3rd Edition - Programming the Open Blockchain
The bitcoinbook repository contains the source code for Mastering Bitcoin, the authoritative open-source book by Andreas M. Antonopoulos on Bitcoin and cryptocurrency technologies. Written in a collaborative and continuously updated format using Markdown and AsciiDoc, the book serves as a comprehensive technical guide for developers, engineers, and system architects who want to understand how Bitcoin works. -
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ML for Beginners
12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all
ML-For-Beginners is a structured, project-driven curriculum that teaches foundational machine learning concepts with approachable math and lots of code. Organized as a multi-week course, it mixes short lectures with labs in notebooks so learners practice regression, classification, clustering, and recommendation techniques on real datasets. Each lesson aims to connect the algorithm to a relatable scenario, reinforcing intuition before diving into parameters, metrics, and trade-offs. The... -
BoldTrail Real Estate CRMBoldTrail, the #1 rated real estate platform, is built to power your entire brokerage with next-generation technology your agents will use and love. Showcase your unique brand with customizable websites for your company, offices, and every agent. Maximize lead capture with a modern, portal-like consumer search experience and intelligent behavior tracking. Hyper-local area pages, home valuation pages and options for rich lifestyle data keep customers searching with your brokerage as the local experts. The most robust lead gen tools on the market help your brokerage, teams & agents effectively drive new business - no matter their budget. Empower your agents to generate free leads instantly with our simple to use landing pages & IDX squeeze pages. Drive more leads with higher quality and lower cost through in-house tools built within the platform. Diversify lead sources with our automated social media posting, integrated Google and Facebook advertising, custom text codes and more.
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nanoGPT
The simplest, fastest repository for training/finetuning models
NanoGPT is a minimalistic yet powerful reimplementation of GPT-style transformers created by Andrej Karpathy for educational and research use. It distills the GPT architecture into a few hundred lines of Python code, making it far easier to understand than large, production-scale implementations. The repo is organized with a training pipeline (dataset preprocessing, model definition, optimizer, training loop) and inference script so you can train a small GPT on text datasets like Shakespeare... -
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Think Python 2
LaTeX source and supporting code for Think Python, 2nd edition
ThinkPython2 is the repository for the second edition of Allen Downey’s Think Python textbook, which teaches programming fundamentals in Python to beginners. The code includes all of the example programs, exercises, and supplementary files referenced in the book, allowing learners to run the examples, experiment, and extend them. The repository contains clean, well-commented Python scripts that are easy to follow and map directly to chapters of the text, covering topics like variables,... -
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Ansible-lint
Best practices checker for Ansible
Ansible Lint is a command-line tool for linting playbooks, roles and collections aimed towards any Ansible users. Its main goal is to promote proven practices, patterns and behaviors while avoiding common pitfalls that can easily lead to bugs or make code harder to maintain. Ansible lint is also supposed to help users upgrade their code to work with newer versions of Ansible. Due to this reason we recommend using it with the newest version of Ansible, even if the version used in production... -
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Web Dev for Beginners
About 24 Lessons, 12 Weeks, Get Started as a Web Developer
...The course also offers global accessibility with translations in more than 40 languages and built-in support for running in GitHub Codespaces or locally in Visual Studio Code. This makes it a practical and engaging way for beginners to gain a solid foundation in web development. -
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Recommenders
Best practices on recommendation systems
The Recommenders repository provides examples and best practices for building recommendation systems, provided as Jupyter notebooks. The module reco_utils contains functions to simplify common tasks used when developing and evaluating recommender systems. Several utilities are provided in reco_utils to support common tasks such as loading datasets in the format expected by different algorithms, evaluating model outputs, and splitting training/test data. Implementations of several... -
Free and Open Source HR SoftwareGive your HR team the tools they need to streamline administrative tasks, support employees, and make informed decisions with the OrangeHRM free and open source HR software.
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ktrain
ktrain is a Python library that makes deep learning AI more accessible
ktrain is a Python library that makes deep learning and AI more accessible and easier to apply. ktrain is a lightweight wrapper for the deep learning library TensorFlow Keras (and other libraries) to help build, train, and deploy neural networks and other machine learning models. Inspired by ML framework extensions like fastai and ludwig, ktrain is designed to make deep learning and AI more accessible and easier to apply for both newcomers and experienced practitioners. With only a few lines... -
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Scientific Visualization
An open access book on scientific visualization using python
The Scientific Visualization book is a freely available open-access textbook that introduces how to produce effective scientific visualizations using Python, focusing especially on leveraging the popular plotting library Matplotlib (and related tools). It goes beyond simple plotting tutorials and emphasizes design principles: how to choose colors, layout subplots, annotate graphs, and present data in a way that is both accurate and visually compelling. As such, it serves as a guide for... -
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PythonPark
Python open source project "The Road to Self-Study Programming"
PythonPark is a large, curated “learning playground” for Python — essentially a comprehensive self-study meta-repository aimed at helping learners progress in Python programming, data science, machine learning, web scraping, and software engineering practices. It aggregates tutorials, learning guides, project examples, and resources across topics: from Python basics and data structures to machine learning, web scraping, and even interview preparation and “programmer life” guidance. Because... -
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Zettel
Zettel allows taking notes from several references and organizing them
Zettel is a program for taking notes from bibliographic references. Instead of marking the text on paper and then going crazy looking for where the copy ended up, the notes are saved in a database, linked to the reference from where they were copied. Notes can be tagged and retrieved in several ways. Zettel é um programa para fichamento de referências bibliográficas. Ao invés de marcar o texto em papel e depois enlouquecer procurando onde a cópia foi parar, as notas são guardadas numa... -
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Simulation of Urban MObility
SUMO is a microscopic, multi-modal traffic simulation.
SUMO is an open source, highly portable, microscopic and continuous traffic simulation package designed to handle large networks. It allows for intermodal simulation including pedestrians and comes with a large set of tools for scenario creation. The code and the issue tracker can be found at https://github.com/eclipse-sumo/sumo/ The documentation can be found at https://sumo.dlr.de/docs/ -
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Advanced Trigonometry Calculator
Precision Trigonometry: Advanced Calculator for Complex Math
Advanced Trigonometry Calculator is equipped with a user-friendly interface that allows for easy input of problems and instant computation. Professionals such as engineers who need to perform advanced trigonometric calculations in their work will find this tool extremely useful. More info by clicking below: https://advantrigoncalc.sourceforge.io/ Advanced Trigonometry Calculator was only and always only developed by the Portuguese Renato Alexandre dos Santos Freitas. Also author of... -
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Prime number ( primenumbers )
Benchmark for 50 000 000 prime numbers as single and multicore
...Surprise, same code in JavaScript for M2 chip in Safari, 12,5 sec single core and 3,3 sec multi core. Python version with numba and numpy on MacOS with M2, 3,78 sec. -
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Summarize from Feedback
Code for "Learning to summarize from human feedback"
The summarize-from-feedback repository implements the methods from the paper “Learning to Summarize from Human Feedback”. Its purpose is to train a summarization model that better aligns with human preferences by first collecting human feedback (comparisons between summaries) to train a reward model, and then fine-tuning a policy (summarizer) to maximize that learned reward. The code includes different stages: a supervised baseline (i.e. standard summarization training), the reward modeling... -
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D2L.ai
Interactive deep learning book with multi-framework code
Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 300 universities from 55 countries including Stanford, MIT, Harvard, and Cambridge. This open-source book represents our attempt to make deep learning approachable, teaching you the concepts, the context, and the code. The entire book is drafted in Jupyter notebooks, seamlessly integrating exposition figures, math, and interactive examples with self-contained code. ... -
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The Art of Programming
A collection of practical tips can be found at the bottom of this page
The Art of Programming (Second Edition) is a curated collection of programming problems and solutions originally derived from the Microsoft 100 Interview Questions blog series, later refined into a long-running tutorial and ultimately a published book. Created by July, the series began in 2010 and has since evolved into an in-depth exploration of algorithmic thinking, data structures, and coding interview preparation. The repository brings together 42 classic programming problems from the... -
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Brain Tokyo Workshop
Experiments and code from Google Brain’s Tokyo research workshop
The Brain Tokyo Workshop repository hosts a collection of research materials and experimental code developed by the Google Brain team based in Tokyo. It showcases a variety of cutting-edge projects in artificial intelligence, particularly in the areas of neuroevolution, reinforcement learning, and model interpretability. Each project explores innovative approaches to learning, prediction, and creativity in neural networks, often through unconventional or biologically inspired methods. The... -
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Catalyst
Accelerated deep learning R&D
Catalyst is a PyTorch framework for accelerated Deep Learning research and development. It allows you to write compact but full-featured Deep Learning pipelines with just a few lines of code. With Catalyst you get a full set of features including a training loop with metrics, model checkpointing and more, all without the boilerplate. Catalyst is focused on reproducibility, rapid experimentation, and codebase reuse so you can break the cycle of writing another regular train loop and make... -
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Machine Learning PyTorch Scikit-Learn
Code Repository for Machine Learning with PyTorch and Scikit-Learn
Initially, this project started as the 4th edition of Python Machine Learning. However, after putting so much passion and hard work into the changes and new topics, we thought it deserved a new title. So, what’s new? There are many contents and additions, including the switch from TensorFlow to PyTorch, new chapters on graph neural networks and transformers, a new section on gradient boosting, and many more that I will detail in a separate blog post. For those who are interested in knowing... -
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Lines
Lines a game written in Python two players through internet
Lines is an old game that I had programed in python (pygame). I usually prefer to use visual c# as a programing language. However, I wrote this game in python in a time period, I was learning python. Here, I want to thank all people who have training videos in youtube, they helped me a lot to make this program. Some of the code of the program is from these videos. The game can be played from one or two persons through internet. The game is a good example for learning pygame. -
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Muc_systray x86
A demonstration python animation system tray X86
This project show you the potential of mini meme on your taskbar. That will help to maximize your OC expose promotion and the the way interaction. Full source code accessible. -
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IPython
Command shell for interactive computing in multiple languages
IPython provides a rich toolkit to help you make the most of using Python interactively. Comprehensive object introspection. IPython provides input history, persistent across sessions. Caching of output results during a session with automatically generated references. Extensible tab completion, with support by default for completion of python variables and keywords, filenames and function keywords. Extensible system of ‘magic’ commands for controlling the environment and performing many...
