Search Results for "computer science"
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Showing 1140 open source projects for "computer science"
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Computer Science Flash Cards
Mini website for testing both general CS knowledge and enforce coding
This repository collects concise flash cards that cover the core ideas of a traditional computer science curriculum with a focus on interview readiness. The cards distill topics like time and space complexity, classic data structures, algorithmic paradigms, operating systems, networking, and databases into short, testable prompts. They are designed for spaced-repetition style study so you can cycle frequently through fundamentals until recall feels automatic. -
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Computer Science courses video lectures
List of Computer Science courses with video lectures
This repository is a curated list of full-length computer science video lecture series across many universities and MOOC platforms, helping learners assemble their own curriculum. The list spans foundational topics like algorithms, data structures, operating systems, computer networks, machine learning, and more, all delivered via lectures rather than just textual tutorials. The contributor guidelines encourage adding high-quality courses (not just casual tutorials) so the list remains academically oriented. ... -
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Crash-Course-Computer-Science-Chinese
Computer Crash Course (40 complete episodes)
Crash-Course-Computer-Science-Chinese is a translation and subtitle project for the full 40-episode Crash Course Computer Science series. It offers high-quality Chinese subtitles (including a “refined” or “proofread” edition) for each episode, making the series more accessible to Chinese-speaking learners. The repository links directly to video uploads on platforms like Bilibili and provides the subtitle text both as a single combined file and as separate files per episode. ... -
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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. -
AI-powered service management for IT and enterprise teamsGive your IT, operations, and business teams the ability to deliver exceptional services—without the complexity. Maximize operational efficiency with refreshingly simple, AI-powered Freshservice.
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Perfect Roadmap To Learn Data Science
Basic To Intermediate Python data science guide
Perfect Roadmap To Learn Data Science In 2025 is an extended, updated learning pathway curated for the modern data-science landscape — blending classical data-analysis, statistics, machine learning, deep learning, computer vision, NLP, as well as current deployment and MLOps practices to prepare learners for data-science careers in 2025. The roadmap is organized to guide learners systematically: starting with Python fundamentals and math/statistics, then progressing through classical machine-learning, deep-learning, data preprocessing, feature engineering, and onto domain-specific applications like computer vision or NLP, ending with deployment, real-world project construction, and best practices for production readiness. ... -
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The Grand Complete Data Science Guide
Data Science Guide With Videos And Materials
The Grand Complete Data Science Materials is a repository curated by a data-science educator that aggregates a wide range of learning resources — from basic programming and math foundation to advanced topics in machine learning, deep learning, natural language processing, computer vision, and deployment practices — into a structured, centralized collection aimed at learners seeking a comprehensive path to data science mastery. -
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jMonkeyEngine
A complete 3-D game development suite written in Java
jMonkeyEngine is a 3-D game engine for adventurous Java developers. It’s open-source, cross-platform, and cutting-edge. v3.6.1 is the latest stable version of the engine. The engine is used by several commercial game studios and computer-science courses. -
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CS-Ebook
Curated list of classic, high-quality computer science books
CS-Ebook is a curated repository that compiles high-quality and classic computer science books across a wide range of software-related fields. It focuses on depth over volume, selecting only well-regarded titles that support structured learning and long-term skill development. It spans core areas such as computer fundamentals, programming languages, software engineering, mathematics, data science, and artificial intelligence, making it suitable for learners at different stages. ... -
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REKCARC-TSC-UHT
Guidance for courses in Department of Computer Science and Technology
This repository is a mirrored, community-curated set of study materials for Tsinghua University’s Department of Computer Science and Technology (the name reads like “THU-CST” reversed). It gathers course notes, labs, exam archives, and project write-ups across core CS topics, emphasizing reproducible setups and guidance for succeeding in demanding classes. The content is organized to mirror the program’s progression so students can see prerequisites and plan learning paths. ... -
AI-generated apps that pass security reviewRetool lets you generate dashboards, admin panels, and workflows directly on your data. Type something like “Build me a revenue dashboard on my Stripe data” and get a working app with security, permissions, and compliance built in from day one. Whether on our cloud or self-hosted, create the internal software your team needs without compromising enterprise standards or control.
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MESHROOM
3D reconstruction software
Photogrammetry is the science of making measurements from photographs. It infers the geometry of a scene from a set of unordered photographies or videos. Photography is the projection of a 3D scene onto a 2D plane, losing depth information. The goal of photogrammetry is to reverse this process. The dense modeling of the scene is the result yielded by chaining two computer vision-based pipelines, “Structure-from-Motion” (SfM) and “Multi View Stereo” (MVS). -
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Papers We Love
Papers from the computer science community to read and discuss
...In addition to the repository, Papers We Love maintains an active Discord server and YouTube channel, fostering ongoing dialogue around the ideas and applications found in computer science literature. Contributions are welcome. -
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OpenRefine
A free, open source, powerful tool for working with messy data
...With OpenRefine you can load data, understand it, clean it up, transform it, reconcile it, and augment it with web services and external data. It allows you to do this all from a web browser and in the convenience and privacy of your own computer. OpenRefine keeps all data securely in your computer by running a small server on it, using your web browser to interact with it. When you're ready, then that is the only time the data will be shared outside of your computer. OpenRefine is available in over 15 languages, is cross-platform and part of the Code for Science & Society. -
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CS-Books
Collection of computer science textbooks, learning materials
CS-Books is a massive curated collection of computer science textbooks, learning materials, and resource links that covers a wide range of topics from programming languages like C/C++ and Python to core subjects such as data structures, algorithms, operating systems, databases, networks, and design patterns. The repository aggregates over a thousand classic reference books and educational resources into a single index, making it a valuable starting point for self-learners, students preparing for technical interviews, and professionals deepening their knowledge across different CS domains. ... -
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Interviews
Comprehensive computer science and interview preparation guide
Interviews is an open source repository designed as a comprehensive guide to preparing for technical interviews, with a strong focus on computer science fundamentals and algorithmic problem solving. Created by Kevin Naughton Jr., the project compiles detailed notes, explanations, and code implementations that cover core areas tested in software engineering interviews. The repository emphasizes topics such as data structures, algorithms, system design, operating systems, databases, and networking. ... -
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Lua
The Lua development repository, as seen by the Lua team
...Lua is designed, implemented, and maintained by a team at PUC-Rio, the Pontifical Catholic University of Rio de Janeiro in Brazil. Lua was born and raised in Tecgraf, formerly the Computer Graphics Technology Group of PUC-Rio. Lua is now housed at LabLua, a laboratory of the Department of Computer Science of PUC-Rio. -
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Coding Interview University
A complete computer science study plan to become a software engineer
...The author used this plan to transition into a Software Development Engineer role at Amazon and emphasizes that most learners will not need to study as many hours to succeed. The repository focuses on practical readiness over memorization, guiding you to learn core computer science topics to about a 75% depth that is sufficient for interviews. It outlines how to choose one primary programming language, recommends foundational books, and explains an effective daily routine of studying concepts then implementing them from scratch. The plan includes extensive topic coverage from algorithms and data structures to systems fundamentals, plus optional advanced areas for deeper growth. -
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AI-Tutorials/Implementations Notebooks
Codes/Notebooks for AI Projects
AI-Tutorials/Implementations Notebooks repository is a comprehensive collection of artificial intelligence tutorials and implementation examples intended for developers, students, and researchers who want to learn by building practical AI projects. The repository contains numerous Jupyter notebooks and code samples that demonstrate modern techniques in machine learning, deep learning, data science, and large language model workflows. It includes implementations for a wide range of AI topics such as computer vision, agent systems, federated learning, distributed systems, adversarial attacks, and generative AI. Many of the tutorials focus on building AI agents, multi-agent systems, and workflows that integrate language models with external tools or APIs. ... -
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Simplified LNCS Template
Improved Lecture Notes in Computer Science (LNCS) template
Improved Lecture Notes in Computer Science (LNCS) template. To build the whole document, execute the provided command. Note that this requires a working Perl installation. In case something goes wrong, you can instruct the LaTeX compiler to stop at the first error. Modern packages such as microtype, cleveref, csquotes, hyperref, hypcap, upquote, natbib, booktabs. -
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InterviewGuide
Repository that collects extensive computer science
InterviewGuide is a widely-starred open-source repository that collects extensive computer science learning notes, interview preparation materials, and job search strategies aimed especially at students and early-career developers. It was created by a developer who documented his own journey from campus to tech industry, including detailed learning pathways for languages like C/C++, Go, JavaScript, and frameworks like Vue, as well as topics such as operating systems, networks, databases, and Redis. ... -
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The Machine & Deep Learning Compendium
List of references in my private & single document
...Originally created as a personal knowledge base, the repository evolved into a public educational resource designed to help learners explore the rapidly expanding machine learning ecosystem. The compendium includes explanations of concepts across multiple domains such as natural language processing, computer vision, time-series analysis, anomaly detection, and graph learning. In addition to technical algorithms, the project also covers practical topics related to data science workflows, engineering practices, and product development in AI systems. -
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Awesome Network Analysis
A curated list of awesome network analysis resources
awesome-network-analysis is a curated list of resources focused on network and graph analysis, including libraries, frameworks, visualization tools, datasets, and academic papers. It covers multiple programming languages and domains like sociology, biology, and computer science. This repository serves as a central reference for researchers, analysts, and developers working with network data. -
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DSA Bootcamp Java
This repository consists of the code samples, assignments, and notes
...The repository is organized into multiple modules that start with Java basics and gradually progress toward arrays, recursion, sorting, searching, linked lists, stacks, queues, trees, graphs, dynamic programming, and other advanced topics. It is a popular resource for students preparing for coding interviews, competitive programming, or computer science fundamentals. -
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Eisvogel
A pandoc LaTeX template to convert markdown files to PDF or LaTeX
A clean pandoc LaTeX template to convert your markdown files to PDF or LaTeX. It is designed for lecture notes and exercises with a focus on computer science. The template is compatible with Pandoc 3. Alternatively, if you don't want to install LaTeX, you can use the Docker image named pandoc/extra. The image contains pandoc, LaTeX, and a curated selection of components such as the eisvogel template, pandoc filters, and open source fonts. -
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CS Self-Learning
Chinese-language CS self-learning guide with curated resources
cs-self-learning is a comprehensive, Chinese-language guide aimed at helping self-learners master computer science concepts. It provides a structured roadmap covering algorithms, operating systems, networks, databases, and more, with curated links to online courses, tutorials, and books. The project is especially useful for non-English speakers pursuing independent or supplemental learning in CS. -
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Kaggle Solutions
Collection of Kaggle Solutions and Ideas
...The repository also highlights important machine learning concepts such as feature engineering, cross-validation strategies, ensemble modeling, and post-processing methods commonly used in winning solutions. Because the content is organized by competition categories such as computer vision, natural language processing, tabular data, and time-series forecasting, users can explore techniques relevant to specific problem types.
