Open Source Linux Education Software - Page 7
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Gen AI apps are built with MongoDB AtlasMongoDB Atlas provides built-in vector search and a flexible document model so developers can build, scale, and run gen AI apps without stitching together multiple databases. From LLM integration to semantic search, Atlas simplifies your AI architecture—and it’s free to get started.
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Level Up Your Cyber Defense with External Threat ManagementMove beyond alerts. Gain full visibility, context, and control over your external attack surface to stay ahead of every threat.
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OpenTeacher is an opensource application that helps you learn a foreign language vocabulary. Just enter some words in your native and foreign language, and OpenTeacher tests you.
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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 poems, reflections and thoughts published on the platform https://www.pensador.com/autor/renato_alexandre_dos_santos_freitas/ ATC is Made in Portugal. He was born in Coimbra on July 6, 1991. He has a degree in Electrical and Telecommunications Engineering and began developing ATC at the beginning of his undergraduate studies. He works for Miclearning, Lda since April 2020. Providing services to Multimac Hito Innovation. -
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AWESOME DATA SCIENCE
Awesome Data Science repository to learn and apply for real world
An open source Data Science repository to learn and apply towards solving real world problems. This is a shortcut path to start studying Data Science. Just follow the steps to answer the questions, "What is Data Science and what should I study to learn Data Science?" Data Science is one of the hottest topics on the Computer and Internet farmland nowadays. People have gathered data from applications and systems until today and now is the time to analyze them. The next steps are producing suggestions from the data and creating predictions about the future. Here you can find the biggest question for Data Science and hundreds of answers from experts. Data scientists combine entrepreneurship with patience, the willingness to build data products incrementally, the ability to explore, and the ability to iterate over a solution. They are inherently interdisciplinary. They can tackle all aspects of a problem, from initial data collection and data conditioning to drawing conclusions. -
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CSS Animation 101
Learn how to bring animation to your web projects
CSS Animation 101 is a tutorial resource that teaches the fundamentals of motion on the web using only CSS. It starts with core building blocks—transitions, transforms, and keyframes—and then layers in timing functions, delays, and choreography to create more expressive sequences. The material stresses clarity and maintainability, showing how to structure styles so animations are easy to reason about and adjust. Throughout, examples demonstrate practical use cases like button feedback, content reveals, loaders, and micro-interactions that enhance user experience without overwhelming it. Performance considerations are discussed, including which properties animate cheaply and how to avoid jank with compositing-friendly transforms. By combining explanation with runnable examples, the resource helps designers and developers develop an intuition for motion that translates directly into everyday interfaces. -
Build Securely on AWS with Proven FrameworksMoving to the cloud brings new challenges. How can you manage a larger attack surface while ensuring great network performance? Turn to Fortinet’s Tested Reference Architectures, blueprints for designing and securing cloud environments built by cybersecurity experts. Learn more and explore use cases in this white paper.
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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 something totally new. Catalyst is compatible with Python 3.6+. PyTorch 1.1+, and has been tested on Ubuntu 16.04/18.04/20.04, macOS 10.15, Windows 10 and Windows Subsystem for Linux. It's part of the PyTorch Ecosystem, as well as the Catalyst Ecosystem which includes Alchemy (experiments logging & visualization) and Reaction (convenient deep learning models serving). -
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Containerization (Apple)
Containerization is a Swift package for running Linux containers
This repository provides educational material and sample code that demystify how containerization works and how container components fit together in practice. It walks through the responsibilities of an image format, registry, and runtime, and shows how a minimal runtime can assemble an isolated process with the right filesystem view, environment, and entrypoint. The samples highlight security hardening considerations—such as process isolation, filesystem scoping, and least-privilege execution—so that containers are not just portable, but safer by default. Developers get a blueprint for taking standard container images and running them in a way that respects platform conventions, tooling, and policies. The emphasis is on clarity and standards alignment rather than building a production-grade engine, which makes the code ideal for learning and experimentation. -
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Convolution arithmetic
A technical report on convolution arithmetic in deep learning
A technical report on convolution arithmetic in the context of deep learning. The code and the images of this tutorial are free to use as regulated by the licence and subject to proper attribution. The animations will be output to the gif directory. Individual animation steps will be output in PDF format to the pdf directory and in PNG format to the png directory. We introduce a guide to help deep learning practitioners understand and manipulate convolutional neural network architectures. The guide clarifies the relationship between various properties (input shape, kernel shape, zero padding, strides and output shape) of convolutional, pooling and transposed convolutional layers, as well as the relationship between convolutional and transposed convolutional layers. Relationships are derived for various cases, and are illustrated in order to make them intuitive. -
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Data Science Specialization
Course materials for the Data Science Specialization on Coursera
The Data Science Specialization Courses repository is a collection of materials that support the Johns Hopkins University Data Science Specialization on Coursera. It contains the source code and resources used throughout the specialization’s courses, covering a broad range of data science concepts and techniques. The repository is designed as a shared space for code examples, datasets, and instructional materials, helping learners follow along with lectures and assignments. It spans essential topics such as R programming, data cleaning, exploratory data analysis, statistical inference, regression models, machine learning, and practical data science projects. By providing centralized resources, the repo makes it easier for students to practice concepts and replicate examples from the curriculum. It also offers a structured view of how multiple disciplines—programming, statistics, and applied data analysis—come together in a professional workflow. -
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DeepMind Educational Resources
DeepMind's repo of educational notebooks for learning AI and research
Educational is an open collection of interactive tutorials created by Google DeepMind to make the fundamentals of machine learning and artificial intelligence accessible to learners of all backgrounds. The repository provides hands-on, beginner-friendly resources that introduce essential AI concepts through Google Colab notebooks, combining intuitive explanations with executable code. The tutorials cover a broad range of topics—from foundational Python programming and data handling to supervised, unsupervised, and reinforcement learning, as well as graph neural networks and scientific reasoning. Specialized notebooks also explore creative AI applications, language modeling, generative models, and protein folding. Each tutorial is designed to be standalone and adaptable for self-study, classroom teaching, or use at summer schools and community workshops. -
Simple, Secure Domain RegistrationRegister or renew your domain and pay only what we pay. No markups, hidden fees, or surprise add-ons. Choose from over 400 TLDs (.com, .ai, .dev). Every domain is integrated with Cloudflare's industry-leading DNS, CDN, and free SSL to make your site faster and more secure. Simple, secure, at-cost domain registration.
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Dict UK
Project to generate POS tag dictionary for Ukrainian language
A Java-based tool for generating full morphological dictionaries for Ukrainian, applying affix rules to base lexemes to produce all inflected forms with part-of-speech tags—used for natural language processing and spell-checking. -
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LeetCode Book
Comprehensive study guide for coding interviews
LeetCode-Book is a comprehensive study guide for coding interviews that consolidates algorithm patterns, data-structure templates, and worked LeetCode solutions. It organizes problems by topic—arrays, linked lists, stacks/queues, trees/graphs, dynamic programming, greedy, backtracking, and math—so you can study systematically. Explanations are concise but intentional, highlighting why a pattern fits, how to reason about boundary cases, and the time/space trade-offs. Many entries include template code and “swap-in” variations, helping you recognize how the same technique adapts across similar problems. The book also emphasizes problem-solving habits like deriving invariants, choosing appropriate data structures, and verifying with small counterexamples. It’s suitable for newcomers building fundamentals and for experienced engineers polishing timing and correctness under interview pressure. -
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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 what this book covers in general, I’d describe it as a comprehensive resource on the fundamental concepts of machine learning and deep learning. The first half of the book introduces readers to machine learning using scikit-learn, the defacto approach for working with tabular datasets. Then, the second half of this book focuses on deep learning, including applications to natural language processing and computer vision. -
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MathModel
Mathematical Modeling for Graduate Students, Mathematical Modeling
MathModel is a repository dedicated to collecting and organizing resources around mathematical modeling, especially for university students preparing for modeling competitions (e.g. undergraduate and graduate levels). The content spans exemplary competition papers, algorithmic solutions, LaTeX templates, presentation slides, MATLAB tutorials, and mind maps of modeling approaches. It acts as a centralized archive where learners can access high-quality past solutions as references, borrow formatting for their own reports, and study algorithmic strategies that performed well in contests. The repository is structured by topic and resource type so that users can more easily find templates, solved problems, and methodological notes. It also includes auxiliary educational materials like references, recommended textbooks, and guidebooks on mathematical modeling theory. -
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Octave
my octave exercises for 2011 stanford machine learning class
This repository is a personal archive of Octave exercises and assignments for the 2011 Stanford Machine Learning class. The author uses the Octave programming environment (which is similar to MATLAB) to implement the homework assignments (ex1 through ex7), providing code solutions, cheat sheets, and scratch files. Octave / MATLAB code illustrating algorithms taught in the class. Cheat sheet for Octave / MATLAB commands. Readme and licensing information. Octave / MATLAB code illustrating algorithms taught in the class. -
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Pinyin
A high-quality solution for converting Chinese to Pinyin
The Chinese to Pinyin tool based on the CC-CEDICT dictionary, more accurately supports the solution of Chinese characters to Pinyin for polyphonic characters. Memory type, suitable for servers with more memory space, advantages, fast conversion. Small memory type (default), suitable for environments with tight memory, advantages, small memory footprint, conversion is not as fast as memory type. I/O type, suitable for virtual machines with strict memory restrictions. Advantages: very minimal memory consumption. Disadvantages, slow conversion, not as fast as memory conversion, php >= 5.5. Small memory type, load dictionary shards into memory. In-memory, preload all dictionaries into memory. Do not load into memory, open dictionary line by line traversal using file stream and allocate single line memory using php5.5 generator (yield) feature. -
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Ray Tracing in One Weekend Book Series
The Ray Tracing in One Weekend series of books
The Ray Tracing in One Weekend series of books are now available to the public for free online. They are now released under the CC0 license. This means that they are as close to public domain as we can get. (While that also frees you from the requirement of providing attribution, it would help the overall project if you could point back to this web site as a service to other users.) These books are formatted for printing directly from your browser, where you can also (on most browsers) save them as PDF. In Ray Tracing in One Weekend, you will build a simple brute-force path tracer. Continuing with Ray Tracing: The Next Week, you will add textures, volumes (like fog), rectangles, instances, lights, and support for lots of objects using a bounding volume hierarchy (BVH). Finally, with Ray Tracing: The Rest Of Your Life, we'll dive into the math of creating a very serious ray tracer. -
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freeCodeCamp
freeCodeCamp.org's open-source codebase and curriculum
freeCodeCamp is a nonprofit educational platform that offers a self-paced curriculum for learning web development, programming, data visualization, APIs, and algorithms. It features interactive coding challenges, real-world projects, and guided progress through topic modules, culminating in certificates for completed tracks. A key aspect is that students contribute to open-source projects for nonprofits or internal tooling as part of their learning, reinforcing both technical and collaborative skills. The community includes discussion forums, local chapters, and peer support, which helps learners break through obstacles and stay motivated. The freeCodeCamp GitHub repository encompasses both the learning platform codebase (frontend, backend, challenge engine) and many support tools, community content, and contributions. Because it is open source, anyone can propose improvements, add lessons, or adapt the platform for new languages and frameworks. -
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MRiLab
A Numerical MRI Simulation Platform
The MRiLab project is moving to GitHub, the latest version can be obtained from https://leoliuf.github.io/MRiLab/ The MRiLab is a numerical MRI simulation package. It has been developed and optimized to simulate MR signal formation, k-space acquisition and MR image reconstruction. MRiLab provides several dedicated toolboxes to analyze RF pulse, design MR sequence, configure multiple transmitting and receiving coils, investigate magnetic field related properties and evaluate real-time imaging technique. The main MRiLab simulation platform combined with those toolboxes can be applied to customize various virtual MR experiments which can serve as a prior stage for prototyping and testing new MR technique and application. If you find MRiLab useful for your work, please cite this paper: Fast Realistic MRI Simulations Based on Generalized Multi-Pool Exchange Tissue Model. IEEE Transactions on Medical Imaging. 2016. doi: 10.1109/TMI.2016.2620961 -
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NET-Simulator was created to help lecturers and students in the study of computer networks. Students can build virtural networks in the virtual environment provided by NET-Simulator. These devices are controlled by means of command line interface.
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GameOver
Training and educating about the web security
About GameOver: Project GameOver was started with the objective of training and educating newbies about the basics of web security and educate them about the common web attacks and help them understand how they work. GameOver has been broken down into two sections. Section 1 consists of special web applications that are designed especially to teach the basics of Web Security. This seciton will cover XSS CSRF RFI & LFI BruteForce Authentication Directory/Path traversal Command execution SQL injection Section 2 is a collection of dileberately insecure Web applications. This section provides a legal platform to test your skills and to try and exploit the vulnerabilities and sharpen your skills before you pentest live sites. We would advice newbies to try and exploit these web applications. These applications provide real life environments and will boost their confidence. -
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A server operating system designed for schools, providing a simple graphical interface for easy installation and maintenance of your network. Also available is a client version that intergrates completely with the server.
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JSmol
JavaScript-Based Molecular Viewer From Jmol
JSmol is the extension of the Java-based molecular visualization applet Jmol (jmol.sourceforge.net) as an HTML5 JavaScript-only web app. It can be used in conjunction with the Java applet to provide an alternative to Java when the platform does not support that (iPhone/iPad) or does not support applets (Android). Used in conjunction with the Jmol JavaScript Object (http://wiki.jmol.org/index.php/Jmol_Javascript_Object), JSmol seamlessly offers alternatives to Java on these non-Applet platforms. JSmol can read all the files that Jmol reads. You can do all the scripting that Jmol does. You can create all the buttons and links and such that you are used to creating for Jmol. All of the rendering capability of the Jmol applet is there. JSmol has both a console and a popup menu. JSmol is integrated fully with JSME and JSpecView. A "lite" version of JSmol provides minimal functionality (balls and sticks only) for extremely small-bandwith apps. Released 1/12/2013 -
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boats
a race scenario drawing tool
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Luz do Saber
Software para alfabetização e inclusão digital.
Luz do Saber é um software educativo utilizado para alfabetização e inclusão digital que conta com duas versões, uma infantil e outra para jovens e adultos. Baseado na metodologia de ensino de Paulo Freire. Este projeto é financiado pelo Governo do Estado do Ceará - Brasil. -
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e-foto
A free GNU/GPL educational digital photogrammetric workstation
A free GNU/GPL educational digital photogrammetric workstation The e-foto Project aims to deliver a comprehensive and accessible digital photogrammetric workstation by offering a complete implementation of the software as free and open-source under the GNU General Public License (GPL). e-foto is equipped with photogrammetric functionalities that empower the creation of topographic tridimensional mapping projects. It supports the utilization of aerial photogrammetric images captured by both analog cameras and digital sensors, enabling professionals to harness diverse data sources for their mapping endeavors. You can access our website at http://www.efoto.eng.uerj.br/ where you will find comprehensive tutorials, thus ensuring a smooth and efficient learning experience.