Open Source Linux Education Software - Page 12
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MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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Flutter Tutorials
Source code for all the tutorials on FilledStacks' channel
This repository is a collection of educational Flutter projects created to teach best practices, patterns, and real-world app features. Each tutorial is structured as a separate mini-project focusing on a topic like state management, navigation, responsive layouts, or service integration. It emphasizes clean architecture, testability, and scalable code, showing developers how to go beyond toy examples. Tutorials often come with detailed commentary or videos to explain design decisions. The collection helps developers bridge the gap between learning Flutter basics and building production-ready apps. It serves as both a reference and a training resource for those aiming to adopt Flutter in professional projects. -
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Hello Python
Comprehensive tutorial repository aimed at teaching the Python program
Hello-Python is a comprehensive tutorial repository aimed at teaching the Python programming language from scratch for beginners. It includes over 100 classes and about 44 hours of video instruction, combined with code samples, projects, and a chat community for support. The material covers the fundamentals—variables, data types, loops, functions—as well as intermediate topics like date handling, list comprehensions, file IO, regular expressions, modules, and packages. The course is designed to be accessible: no prior programming experience required, and the resources are freely available. 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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JCSprout
Basic, concurrent algorithm
JCSprout is a curated learning path for Java engineers that mixes concise notes, diagrams, and runnable examples to cover core computer science and JVM topics. It walks readers through data structures and algorithms, networking fundamentals, Java concurrency, JVM memory model and GC, and common interview problem patterns. The repository emphasizes understanding over memorization, linking conceptual summaries with small code artifacts that can be compiled and profiled. It also highlights best practices around collections, thread safety, lock-free techniques, and performance trade-offs so learners can make informed design decisions. The material is structured to support incremental study, making it useful for both foundational learning and pre-interview refreshers. By bundling explanations with code and references, JCSprout acts as a compact “from student to engineer” handbook for the Java ecosystem. -
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Kubernetes The Hard Way
Bootstrap Kubernetes the hard way
Kubernetes The Hard Way is a hands-on guide to building a Kubernetes cluster from the ground up without using automation tools—no managed services, no scripts to hide the details. It walks you through every component: provisioning compute resources, generating TLS certificates, configuring etcd, bootstrapping the control plane, joining worker nodes, setting networking, and verifying everything works. The purpose is educational: by doing each step manually, you gain deep insight into how Kubernetes works under the hood—control plane components, kube-configs, networking, encryption, etc. The guide isn’t meant for production use; rather it’s a learning tool to build foundational understanding before using higher-level platforms. You’ll learn about certificate management, API server flags, etcd clustering, kubelet boot sequence, and how pods route traffic across nodes. Many practitioners use it to prep for Kubernetes certifications or deeply understand Kubernetes internals. -
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LLM Course
Course to get into Large Language Models (LLMs)
LLM Course is a hands-on, notebook-driven path for learning how large language models work in practice, from data curation to training, fine-tuning, evaluating, and deploying. It emphasizes reproducible experiments: each step is demonstrated with runnable code, clear dependencies, and references to commonly used open-source models and libraries. Learners get exposure to multiple adaptation strategies—LoRA/QLoRA, instruction fine-tuning, and alignment techniques—so they can choose approaches that fit their hardware and budgets. The materials also cover inference optimization and quantization to make serving LLMs feasible on commodity GPUs or even CPUs, which is crucial for side projects and startups. Evaluation is treated as a first-class topic, with examples of automatic and human-in-the-loop methods to catch regressions and verify quality beyond simple loss values. By the end, students have a mental model and a practical toolkit for iterating on datasets, training configs, etc. -
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Learning Bitcoin from the Command Line
A complete course for learning Bitcoin programming and usage
Learning Bitcoin from the Command Line is a tutorial for working with Bitcoin (and Lightning) that teaches direct interaction with the servers themselves, as the most robust and secure way to begin cryptocurrency work. This is a draft in progress, so that I can get some feedback from early reviewers. It is not yet ready for use. Learning Bitcoin from the Command Line is a project of Blockchain Commons. We are proudly a "not-for-profit" social benefit corporation committed to open source & open development. Our work is funded entirely by donations and collaborative partnerships with people like you. Every contribution will be spent on building open tools, technologies, and techniques that sustain and advance blockchain and internet security infrastructure and promote an open web. We are also tentatively considering what we could include in a v3.0 of the course. -
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Learning to Learn in TensorFlow
Learning to Learn in TensorFlow
Learning to Learn, created by Google DeepMind, is an experimental framework that implements meta-learning—training neural networks to learn optimization strategies themselves rather than relying on manually designed algorithms like Adam or SGD. The repository provides code for training and evaluating learned optimizers that can generalize across different problem types, such as quadratic functions and image classification tasks (MNIST and CIFAR-10). Using TensorFlow, it defines a meta-optimizer model that learns by observing and adapting to the optimization trajectories of other models. The project allows users to compare performance between traditional optimizers and the learned optimizer (L2L) on various benchmarks, demonstrating how optimization strategies can be learned through experience. The design supports both single-variable and high-dimensional problems, and includes tools for evaluating how well a learned optimizer performs on unseen tasks. -
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Little Book of Linear Algebra
A concise, beginner-friendly introduction to the core ideas of linear
This is a concise, beginner-friendly introduction to the fundamental concepts of linear algebra, intended to give readers intuition without overwhelming detail. The material is organized into chapters covering vectors, matrices, linear systems, vector spaces, eigenvalues/eigenvectors, and other central topics, each with worked examples and explanations. There is also a companion “LAB” section for hands-on exploration (e.g. using Python/NumPy) to help cement the connections between algebraic formulas and computational behavior. The exposition aims to sit between a pop-math summary and a heavy textbook: definitions and key theorems are stated cleanly, while proofs are sometimes omitted or sketched to keep the flow digestible. Because of its brevity and clarity, it's especially useful as a first pass for learners who want a solid map of the subject before diving into full textbooks. -
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Mall Learning
Tutorial and sample-code repository
mall-learning is a tutorial and sample-code repository that explores an entire e-commerce system architecture from backend to frontend. The associated “mall” project (with tens of thousands of stars) is an open-source full-stack e-commerce platform built with Spring Boot, MyBatis, Elasticsearch, RabbitMQ, Redis, MongoDB, MySQL and containerized via Docker. The learning repository breaks down architecture, business modules (products, orders, marketing, members), deployment (Linux, Docker, Jenkins) and technical points in detail. For each topic it provides sample code, explanations of why design decisions were made, trade-offs and how to implement real-world features like search, caching, delayed messaging, file storage. It’s aimed at developers who want to understand how to build scalable e-commerce systems rather than just copy-paste modules. With clearly organized “tiny” modules (mall-tiny-01 etc) the repo supports step-by-step incremental learning. -
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Mastering Shiny
Mastering Shiny: a book
Mastering Shiny is a book (and its accompanying source repository) by Hadley Wickham that teaches people how to build interactive web applications using Shiny in R. It starts from basics (your first app, UI components, reactivity) and progresses to more advanced topics (dynamic UIs, modules, testing, security, performance). It is intended to help data scientists, analysts, or R users who may not have deep experience in web technologies become expert Shiny developers. The source code is open, and the book is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. -
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Open Source Vizier
Python-based research interface for blackbox
Open Source (OSS) Vizier is a Python-based interface for blackbox optimization and research, based on Google’s original internal Vizier, one of the first hyperparameter tuning services designed to work at scale. Allows a user to setup an OSS Vizier Server, which can host black-box optimization algorithms to serve multiple clients simultaneously in a fault-tolerant manner to tune their objective functions. Defines abstractions and utilities for implementing new optimization algorithms for research and to be hosted in the service. A wide collection of objective functions and methods to benchmark and compare algorithms. Define a problem statement and study configuration. Setup a local server, setup a client to connect to the server, perform a typical tuning loop, and use other client APIs. -
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Operating Systems: From 0 to 1
A book to gain the foundational knowledge to write operating systems
This book helps you gain the foundational knowledge required to write an operating system from scratch. Hence the title, 0 to 1. After completing this book, at the very least you will learn how to write an operating system from scratch by reading hardware datasheets. In the real world, it works like that. You won't be able to consult Google for a quick answer. A big picture of how each layer of a computer is related to the other, from hardware to software. Write code independently. It's pointless to copy and paste code. Real learning happens when you solve problems on your own. Some examples are given to kick start, but most problems are yours to conquer. However, the solutions are available online for you to examine after giving it a good try. The book does not try to teach you everything, but enough to enable you to learn by yourself. Once you master part 1 and part 2 (which consist of 8 chapters), you can drop the book and learn by yourself. -
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Oppia
A free, online learning platform to make quality education accessible
Oppia is an online learning tool that enables anyone to easily create and share interactive activities (called 'explorations'). These activities simulate a one-on-one conversation with a tutor, making it possible for students to learn by doing while getting feedback. Oppia identifies common wrong answers and provides tailored feedback, so that students get a personalized experience. Our lessons keep students engaged through playful characters and use different strategies to solidify their knowledge. Check out our math lessons with proven results! In addition to developing the Oppia platform, the team is also developing and piloting a set of free and effective lessons on basic mathematics. These lessons are targeted at learners who lack access to educational resources. Oppia is written using Python and AngularJS, and is built on top of Google App Engine. -
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PRIMA
PRIMA is a package for solving general nonlinear optimization problems
PRIMA is a package for solving general nonlinear optimization problems without using derivatives. It provides the reference implementation for Powell's derivative-free optimization methods, i.e., COBYLA, UOBYQA, NEWUOA, BOBYQA, and LINCOA. PRIMA means Reference Implementation for Powell's methods with Modernization and Amelioration, P for Powell. PRIMA is a package for solving general nonlinear optimization problems without using derivatives. It provides the reference implementation for Powell's renowned derivative-free optimization methods, i.e., COBYLA, UOBYQA, NEWUOA, BOBYQA, and LINCOA. The "P" in the name stands for Powell, and "RIMA" is an acronym for "Reference Implementation with Modernization and Amelioration". -
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PyTorch Book
PyTorch tutorials and fun projects including neural talk
This is the corresponding code for the book "The Deep Learning Framework PyTorch: Getting Started and Practical", but it can also be used as a standalone PyTorch Getting Started Guide and Tutorial. The current version of the code is based on pytorch 1.0.1, if you want to use an older version please git checkout v0.4or git checkout v0.3. Legacy code has better python2/python3 compatibility, CPU/GPU compatibility test. The new version of the code has not been fully tested, it has been tested under GPU and python3. But in theory there shouldn't be too many problems on python2 and CPU. The basic part (the first five chapters) explains the content of PyTorch. This part introduces the main modules in PyTorch and some tools commonly used in deep learning. For this part of the content, Jupyter Notebook is used as a teaching tool here, and readers can modify and run with notebooks and repeat experiments. -
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Python Mastery (Course)
Advanced Python Mastery
python-mastery is a collection of course materials created by David Beazley for teaching advanced Python programming concepts. It emphasizes deep understanding through real-world coding exercises and topics like generators, decorators, closures, and metaclasses. The repository is designed for learners who already know the basics of Python and want to push their skills to an expert level. -
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React Projects
Collection of React example / tutorial projects
This repository is a collection of small-to-medium React applications that showcase practical patterns, from stateful widgets to multi-page interfaces. Each project is self-contained and demonstrates specific techniques—hooks for state and effects, context for global state, custom hooks for reuse, and reducer patterns for complex updates. The codebases include common UI tasks like forms, modals, lists, filtered search, and pagination, along with data fetching and basic routing where appropriate. Because every project focuses on a narrow set of concepts, learners can clone a single folder, run it, and grasp the idea without wading through a monolith. The examples are structured to be approachable yet idiomatic, encouraging good habits while staying close to what developers actually build. Over time, the collection functions as a portfolio of reference implementations that students and practitioners can adapt to new projects. -
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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 state-of-the-art algorithms are included for self-study and customization in your own applications. Please see the setup guide for more details on setting up your machine locally, on a data science virtual machine (DSVM) or on Azure Databricks. Independent or incubating algorithms and utilities are candidates for the contrib folder. This will house contributions which may not easily fit into the core repository or need time to refactor or mature the code and add necessary tests. -
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SICP PDF
SICP PDF with Texinfo and LaTeX source
sicp-pdf is a LaTeX-based PDF version of Structure and Interpretation of Computer Programs (SICP), the classic textbook by Harold Abelson, Gerald Jay Sussman, and Julie Sussman. It builds upon the earlier Unofficial Texinfo Format (UTF), which itself was derived from the MIT Press HTML version, but enhances the project by fully converting the source into LaTeX. This conversion allows for high-quality typesetting, improved design options, and the integration of OpenType and Unicode features through XeTeX. The repository contains both Texinfo and LaTeX sources, with automated scripts to keep them in sync during builds. Users can recompile the book locally with a recent TeX Live distribution and the necessary fonts, while Inkscape is required for SVG-to-PDF image conversions. The project is continuously refined to address formatting issues and ensure the text and figures render correctly across platforms. -
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SLAMBook-en
The English version of 14 lectures on visual SLAM
This project is the English version of “14 Lectures on Visual SLAM: From Theory to Practice,” a text and teaching resource about visual simultaneous localization and mapping (SLAM). It provides the full LaTeX source (formerly Markdown) for all 14 chapters, letting readers compile and study the material systematically. Within the repository you’ll find organized subfolders (e.g. chapters, latex, resources) containing the lecture contents, references, figures, and supporting assets for each part of the course. The material covers both foundational theory—geometry, camera models, feature matching, pose estimation—and practical systems components such as optimization, map representation, loop closure, and real-time factors. It aims to bridge academic rigor with hands-on implementation: readers are encouraged to implement algorithms, read code, and follow along with the mathematical derivations and system design decisions. -
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SRE Checklist
A checklist of anyone practicing Site Reliability Engineering
SRE Checklist is a practical, operations-focused checklist for running reliable services the way Site Reliability Engineering recommends. It breaks SRE good practices into concrete items: SLIs/SLOs, alerting, runbooks, on-call processes, capacity planning, backups, security, and incident response. Instead of only describing SRE theory, it turns it into “did you do this yet?” items that teams can track as they harden their systems. This makes it especially helpful for organizations that are new to SRE and want to operationalize reliability without inventing everything from scratch. The checklist format also makes audits and maturity assessments straightforward: you can see at a glance what’s in place and what’s missing. Over time, teams can adopt it as part of their service-readiness or launch checklists. -
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Scala Exercises
The easy way to learn Scala
Scala Exercises is an open source platform for learning Scala through interactive exercises and tutorials. It provides a hands-on learning environment where users can read theory and immediately code (in the browser) and see results. The content covers Scala language features, functional programming libraries (cats, scalaz, etc.), and fp-style patterns. It is modular, so additional modules or “sections” can be added for new topics or libraries. The aim is to reduce the friction in learning Scala by integrating documentation, examples, and live code execution in a unified environment. It also serves as a community-driven repository: contributors can author and maintain exercises, and learners can see evolving content aligned with the Scala ecosystem’s changes. -
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SpaceVim
A community-driven modular vim/neovim distribution
SpaceVim is a community-driven modular Vim distribution. It manages collections of plugins in layers, which help to collect related packages together to provide IDE-like features. First of all, you need to install Vim or Neovim, preferably with +python3 support enabled. Also, you need to have git and curl installed in your system, which is needed for downloading plugins and fonts. If you are using a terminal emulator, you will need to set the font in the terminal configuration. After SpaceVim is installed, launch nvim or vim, all plugins will be downloaded automatically. The easiest way is to download install it. cmd and run it as administrator or install SpaceVim manually. If you want to use vim script to configure SpaceVim, please check out the bootstrap function section. The SpaceVim Documentation will introduce you to the main topics important to using SpaceVim. -
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Statistics for Data Scientists
"Statistics for Data Scientists: 50 Essential Concepts"
The “statistics-for-data-scientists” repository is a pedagogical resource designed to bridge rigorous statistics theory and practical data science workflows. The code and materials are intended to help data scientists and analysts grasp statistical principles (e.g. inference, regressions, hypothesis testing, probability, confidence intervals) in contexts relevant to real data analysis tasks. The repository includes Jupyter notebooks, R scripts, worked examples, and possibly problem sets that illustrate how statistical methods are applied to real datasets. It aims to demystify the bridge between textbook statistics and empirical modeling by walking through assumption checking, visualization, interpreting outputs, and pitfalls of misuse. Throughout, the content emphasizes clarity and accessibility, showing not just how to run statistical tests or build models, but what they mean and when one method is preferred over another. -
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Swift Guide
Swift Featured Projects in brain Mapping
SwiftGuide is a comprehensive, community-maintained guide to the Swift programming language, designed to serve as both a learning resource and a handy reference. It covers all major language aspects: syntax, control flow, functions, closures, generics, protocols, extensions, memory management, concurrency, and the standard library. Each topic typically includes clear explanations, annotated code snippets, and tips for best practices, helping readers understand both how features work and how to use them idiomatically. Over time, the guide has evolved alongside Swift itself, with updates to reflect new language releases, deprecations, and shifting patterns. It also collects references to additional resources, external libraries, and community articles, making it a kind of curated gateway. Because it’s open content, readers can contribute corrections, translations, or expansions to keep it current.