Table of Contents
Google Images Download
DeepFaceLab
空气流动
Xonsh
ML-Agents
XSStrike
NeutralTalk
中立对话
Manim
TensorFlow项目
地图模型导入器
in conclusion
Home Backend Development Python Tutorial What are some good Python projects on GitHub?

What are some good Python projects on GitHub?

Aug 19, 2023 am 11:53 AM
machine learning data analysis ai learning

What are some good Python projects on GitHub?

Among the community of developers and programmers, Python is the most popular and in-demand programming language. Around 73 million developers may access an open-source community using Git repositories through GitHub. Python projects are highly sought after to effectively boost programming language expertise, and GitHub can help with that. From building a straightforward password generator to automating repetitive jobs and mining Twitter Data, the repository has something for everyone.

让我们来看一些当前流行的GitHub开源Python项目。

Google Images Download

Hundreds of Google photos may be searched for and downloaded with this command-line Python tool. The script has the ability to search for words and phrases and, if desired, download picture assets. Python versions 2.x and 3.x are compatible with Google Pictures Download. You can study the project's source code to improve your programming abilities and comprehend how it applies in actual situations.

DeepFaceLab

的翻译为中文为:

DeepFaceLab

“Iperov”开发了用于人脸交换的开源DeepFaceLab技术。它提供了一个必要且简单的流程,任何人都可以使用,而无需完全理解深度学习框架或创建模型。该系统提供了一种灵活且松散的耦合结构,用户可以在自己的流程中添加更多功能,而无需编写冗长的样板代码。

空气流动

The Python open-source project Airflow offers a variety of REST API endpoints across the objects and is available on GitHub. JSON is accepted as input, and JSON is also returned as output. Backward compatibility with Python programs is included in the Airflow APIs.

Xonsh

的中文翻译为:

Xonsh

像Unix这样的命令行解释器对于交互式程序是必需的。这些操作系统使用shell脚本来控制执行。现在,如果你的shell能够理解一种更可扩展的编程语言,而不是不得不妥协,那不是更实用吗?这就是Xonsh(发音为"Konk")的用武之地。

它是一个运行在Python之上的提示符shell语言。这个跨平台语言拥有庞大的标准库和各种变量类型,使得编写脚本变得简单。Xonsh还使用了一个名为vox的虚拟环境管理系统。

ML-Agents

一个名为Unity机器学习代理工具包(ML-Agents)的开源项目使得使用模拟和游戏作为智能代理的训练场成为可能。通过易于使用的Python API,可以使用强化学习、模仿学习、神经进化或其他机器学习技术来教授代理。支持各种环境设置和训练情境,可定制的Unity SDK以及内置的模仿学习支持仅是其众多功能之一。

XSStrike

的中文翻译为:

XSStrike

The Python programming language's XSStrike project is one of the most popular ones on GitHub and is well-known for its ability to identify and counteract XSS assaults. A fast crawler, an intelligent payload generator, four handwritten parsers, and a fuzzing engine are among its further features.

NeutralTalk

的中文翻译为:

中立对话

Using NeutralTalk, you can hone your understanding of multimodal recurrent neural networks. It is an image description-focused Python and NumPy project.

自然语言处理和计算机视觉经常被用于创建图片标题的方法中。该系统具有理解情境并提供照片中显示信息的描述的能力。

NeutralTalk2 可用于找到最新的字幕代码。这个项目比上一个项目更快,因为使用了轻量级且高级的编程语言 Lua 来创建它。

Manim

的翻译为:

Manim

Manim是一个用于创建图形化数学教程的工具。它运行在Python 3.7上,并且主要利用编程来生成精确的动画。Manim使用Python以编程方式创建动画,允许完全控制每个动画的执行方式。

TensorFlow项目

与开源机器学习框架一起,TensorFlow项目是受欢迎的开源Python GitHub项目之一。它提供了高性能数值计算的指导,具有可适应的架构和简单的计算部署,适用于多个平台。

地图模型导入器

Using vast maps, the Maps Models Importer imports 3D models. Only a Blender add-on makes up this experimental technology, and 3D content programs like Google Maps are needed to complete the process. Learn how to import models from Google Maps with the help of this project.

in conclusion

In conclusion, Python’s popularity in the developer community is obvious, and GitHub provides an open source platform for engineers to collaborate and develop their capabilities. The most popular open source Python projects on GitHub demonstrate Python’s flexibility in different areas, including deep learning, data mining, and game development. From Google Image downloads to TensorFlow, these projects provide exciting opportunities to practice programming skills, explore new technologies, and collaborate with a large community of engineers. As demand for Python continues to grow, these projects will undoubtedly continue to evolve and inspire new possibilities in programming.

The above is the detailed content of What are some good Python projects on GitHub?. For more information, please follow other related articles on the PHP Chinese website!

Statement of this Website
The content of this article is voluntarily contributed by netizens, and the copyright belongs to the original author. This site does not assume corresponding legal responsibility. If you find any content suspected of plagiarism or infringement, please contact admin@php.cn

Hot AI Tools

Undresser.AI Undress

Undresser.AI Undress

AI-powered app for creating realistic nude photos

AI Clothes Remover

AI Clothes Remover

Online AI tool for removing clothes from photos.

Undress AI Tool

Undress AI Tool

Undress images for free

Clothoff.io

Clothoff.io

AI clothes remover

AI Hentai Generator

AI Hentai Generator

Generate AI Hentai for free.

Hot Article

R.E.P.O. Energy Crystals Explained and What They Do (Yellow Crystal)
1 months ago By 尊渡假赌尊渡假赌尊渡假赌
R.E.P.O. Best Graphic Settings
1 months ago By 尊渡假赌尊渡假赌尊渡假赌
R.E.P.O. How to Fix Audio if You Can't Hear Anyone
1 months ago By 尊渡假赌尊渡假赌尊渡假赌
R.E.P.O. Chat Commands and How to Use Them
1 months ago By 尊渡假赌尊渡假赌尊渡假赌

Hot Tools

Notepad++7.3.1

Notepad++7.3.1

Easy-to-use and free code editor

SublimeText3 Chinese version

SublimeText3 Chinese version

Chinese version, very easy to use

Zend Studio 13.0.1

Zend Studio 13.0.1

Powerful PHP integrated development environment

Dreamweaver CS6

Dreamweaver CS6

Visual web development tools

SublimeText3 Mac version

SublimeText3 Mac version

God-level code editing software (SublimeText3)

This article will take you to understand SHAP: model explanation for machine learning This article will take you to understand SHAP: model explanation for machine learning Jun 01, 2024 am 10:58 AM

In the fields of machine learning and data science, model interpretability has always been a focus of researchers and practitioners. With the widespread application of complex models such as deep learning and ensemble methods, understanding the model's decision-making process has become particularly important. Explainable AI|XAI helps build trust and confidence in machine learning models by increasing the transparency of the model. Improving model transparency can be achieved through methods such as the widespread use of multiple complex models, as well as the decision-making processes used to explain the models. These methods include feature importance analysis, model prediction interval estimation, local interpretability algorithms, etc. Feature importance analysis can explain the decision-making process of a model by evaluating the degree of influence of the model on the input features. Model prediction interval estimate

Identify overfitting and underfitting through learning curves Identify overfitting and underfitting through learning curves Apr 29, 2024 pm 06:50 PM

This article will introduce how to effectively identify overfitting and underfitting in machine learning models through learning curves. Underfitting and overfitting 1. Overfitting If a model is overtrained on the data so that it learns noise from it, then the model is said to be overfitting. An overfitted model learns every example so perfectly that it will misclassify an unseen/new example. For an overfitted model, we will get a perfect/near-perfect training set score and a terrible validation set/test score. Slightly modified: "Cause of overfitting: Use a complex model to solve a simple problem and extract noise from the data. Because a small data set as a training set may not represent the correct representation of all data." 2. Underfitting Heru

Transparent! An in-depth analysis of the principles of major machine learning models! Transparent! An in-depth analysis of the principles of major machine learning models! Apr 12, 2024 pm 05:55 PM

In layman’s terms, a machine learning model is a mathematical function that maps input data to a predicted output. More specifically, a machine learning model is a mathematical function that adjusts model parameters by learning from training data to minimize the error between the predicted output and the true label. There are many models in machine learning, such as logistic regression models, decision tree models, support vector machine models, etc. Each model has its applicable data types and problem types. At the same time, there are many commonalities between different models, or there is a hidden path for model evolution. Taking the connectionist perceptron as an example, by increasing the number of hidden layers of the perceptron, we can transform it into a deep neural network. If a kernel function is added to the perceptron, it can be converted into an SVM. this one

The evolution of artificial intelligence in space exploration and human settlement engineering The evolution of artificial intelligence in space exploration and human settlement engineering Apr 29, 2024 pm 03:25 PM

In the 1950s, artificial intelligence (AI) was born. That's when researchers discovered that machines could perform human-like tasks, such as thinking. Later, in the 1960s, the U.S. Department of Defense funded artificial intelligence and established laboratories for further development. Researchers are finding applications for artificial intelligence in many areas, such as space exploration and survival in extreme environments. Space exploration is the study of the universe, which covers the entire universe beyond the earth. Space is classified as an extreme environment because its conditions are different from those on Earth. To survive in space, many factors must be considered and precautions must be taken. Scientists and researchers believe that exploring space and understanding the current state of everything can help understand how the universe works and prepare for potential environmental crises

Implementing Machine Learning Algorithms in C++: Common Challenges and Solutions Implementing Machine Learning Algorithms in C++: Common Challenges and Solutions Jun 03, 2024 pm 01:25 PM

Common challenges faced by machine learning algorithms in C++ include memory management, multi-threading, performance optimization, and maintainability. Solutions include using smart pointers, modern threading libraries, SIMD instructions and third-party libraries, as well as following coding style guidelines and using automation tools. Practical cases show how to use the Eigen library to implement linear regression algorithms, effectively manage memory and use high-performance matrix operations.

Explainable AI: Explaining complex AI/ML models Explainable AI: Explaining complex AI/ML models Jun 03, 2024 pm 10:08 PM

Translator | Reviewed by Li Rui | Chonglou Artificial intelligence (AI) and machine learning (ML) models are becoming increasingly complex today, and the output produced by these models is a black box – unable to be explained to stakeholders. Explainable AI (XAI) aims to solve this problem by enabling stakeholders to understand how these models work, ensuring they understand how these models actually make decisions, and ensuring transparency in AI systems, Trust and accountability to address this issue. This article explores various explainable artificial intelligence (XAI) techniques to illustrate their underlying principles. Several reasons why explainable AI is crucial Trust and transparency: For AI systems to be widely accepted and trusted, users need to understand how decisions are made

Five schools of machine learning you don't know about Five schools of machine learning you don't know about Jun 05, 2024 pm 08:51 PM

Machine learning is an important branch of artificial intelligence that gives computers the ability to learn from data and improve their capabilities without being explicitly programmed. Machine learning has a wide range of applications in various fields, from image recognition and natural language processing to recommendation systems and fraud detection, and it is changing the way we live. There are many different methods and theories in the field of machine learning, among which the five most influential methods are called the "Five Schools of Machine Learning". The five major schools are the symbolic school, the connectionist school, the evolutionary school, the Bayesian school and the analogy school. 1. Symbolism, also known as symbolism, emphasizes the use of symbols for logical reasoning and expression of knowledge. This school of thought believes that learning is a process of reverse deduction, through existing

Is Flash Attention stable? Meta and Harvard found that their model weight deviations fluctuated by orders of magnitude Is Flash Attention stable? Meta and Harvard found that their model weight deviations fluctuated by orders of magnitude May 30, 2024 pm 01:24 PM

MetaFAIR teamed up with Harvard to provide a new research framework for optimizing the data bias generated when large-scale machine learning is performed. It is known that the training of large language models often takes months and uses hundreds or even thousands of GPUs. Taking the LLaMA270B model as an example, its training requires a total of 1,720,320 GPU hours. Training large models presents unique systemic challenges due to the scale and complexity of these workloads. Recently, many institutions have reported instability in the training process when training SOTA generative AI models. They usually appear in the form of loss spikes. For example, Google's PaLM model experienced up to 20 loss spikes during the training process. Numerical bias is the root cause of this training inaccuracy,

See all articles