Open Source Linux Education Software - Page 67
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Education Software for Linux
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Software de Mapas de Argumentos con interfaz gráfica diseñado con el objetivo específico (aunque no excluyente) de analizar la estructura argumentativa de demostraciones matemáticas, utilizando el modelo de Toulmin. Además, el contenido de los elementos puede ser tanto texto como imágenes, lo que permite guardar como imagen expresiones matemáticas escritas en LaTeX (utilizando software de apoyo como KLatexFormula). Se puede imprimir el diagrama y exportarlo a formato imagen (varios formatos) o generar un fichero HTML que contenga el diagrama y su contenido. Incorpora la herramienta "Estadísticas" que permite calcular totales, máximo, mínimos y medias de los elementos de varias demostraciones guardadas.
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Debiance is an innovative platform to test ICT Linux skills. It is based on Python, PostgreSQL, Zope and Plone. Tests are multiple-answer and single-choice, together with practical terminal sessions.
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An application which will calculate how long it will take to pay off a credit card at a given interest rate and payment amount, and how much of what is paid will go toward interest.
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DecipherAxe
Academic deciphering aiding tool, for ancient languages
Scientific academic deciphering aiding tool, for ancient languages. The project was originally programmed on: 21/08/21 at 20:58. Currently in BETA, a state with a "Ugaritic cuneiform" feature for transliteration, form only at the moment. Others will be added soon. Supports: Windows & Linux. Runs in WINE for others. -
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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Deep Learning
Deep Learning Book Chinese Translation
With the help and proofreading of many netizens, the Chinese version was finally published. Although there are still many problems, at least 90% of the content is readable and accurate. We have preserved the meaning of the original book Deep Learning as much as possible and retained the original language of the book. However, our level is limited, and we cannot eliminate the variance of many readers. We still need everyone's advice and help to reduce translation bias together. All you have to do is read, then aggregate your suggestions and raise issues (preferably not one by one). If you are sure that your suggestion does not need to be discussed, you can directly initiate a PR. Please download the PDF directly to read. We do not intend to provide formats such as EPUB. Please modify it yourself if necessary. -
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Deep Learning course
Slides and Jupyter notebooks for the Deep Learning lectures
Slides and Jupyter notebooks for the Deep Learning lectures at Master Year 2 Data Science from Institut Polytechnique de Paris. This course is being taught at as part of Master Year 2 Data Science IP-Paris. Note: press "P" to display the presenter's notes that include some comments and additional references. This lecture is built and maintained by Olivier Grisel and Charles Ollion. -
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Deep Learning for Medical Applications
Deep Learning Papers on Medical Image Analysis
Deep-Learning-for-Medical-Applications is a repository that compiles deep learning methods, code implementations, and examples applied to medical imaging and healthcare data. The project addresses domain-specific challenges like segmentation, classification, detection, and multimodal data (e.g. MRI, CT, X-ray) using state-of-the-art architectures (e.g. U-Net, ResNet, GAN variants) tailored to medical constraints (small datasets, annotation costs, class imbalance). It includes Jupyter notebooks, model architectures, data preprocessing pipelines, and evaluation scripts specific to medical imaging tasks. The repository may also contain domain-specific modules: loss functions like Dice, focal loss, metrics such as sensitivity/recall/IoU, and visualization utilities for overlaying segmentation masks. -
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DeepLearning
Deep Learning (Flower Book) mathematical derivation
" Deep Learning " is the only comprehensive book in the field of deep learning. The full name is also called the Deep Learning AI Bible (Deep Learning) . It is edited by three world-renowned experts, Ian Goodfellow, Yoshua Bengio, and Aaron Courville. Includes linear algebra, probability theory, information theory, numerical optimization, and related content in machine learning. At the same time, it also introduces deep learning techniques used by practitioners in the industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling and practical methods, and investigates topics such as natural language processing, Applications in speech recognition, computer vision, online recommender systems, bioinformatics, and video games. Finally, the Deep Learning book provides research directions covering theoretical topics including linear factor models, autoencoders, representation learning, structured probabilistic models, etc. -
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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. -
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DeepMind Lab
A customizable 3D platform for agent-based AI research
DeepMind Lab is a 3D learning environment based on id Software's Quake III Arena via ioquake3 and other open source software. DeepMind Lab provides a suite of challenging 3D navigation and puzzle-solving tasks for learning agents. Its primary purpose is to act as a testbed for research in artificial intelligence, especially deep reinforcement learning. If you use DeepMind Lab in your research and would like to cite the DeepMind Lab environment, we suggest you cite the DeepMind Lab paper. To enable compiler optimizations, pass the flag --compilation_mode=opt, or -c opt for short, to each bazel build, bazel test and bazel run command. The flag is omitted from the examples here for brevity, but it should be used for real training and evaluation where performance matters. DeepMind Lab ships with an example random agent in python/random_agent.py which can be used as a starting point for implementing a learning agent. -
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DeepSqueak
DeepSqueak Using Machine Vision to Accelerate Bioacoustics Research
Using Machine Vision to Accelerate Bioacoustics Research. -
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Delphi Learning Package for Moodle
Delphi Learning Package is a tool for collaborative learning in moodle
Delphi Learning Package (DLP) is a tool for collaborative learning. The methodology implemented using DLP is based on the well known strategic consultancy method called Delphi. See http://en.wikipedia.org/wiki/Delphi_method The Delphi method is a structured communication technique, originally developed as a systematic, interactive forecasting method which relies on a panel of experts.The experts answer questionnaires in two or more rounds. After each round, a facilitator provides an anonymous summary of the experts’ forecasts from the previous round as well as the reasons they provided for their judgments. Using DLP you can integrate a Delphi communication process into your learning environment as part as a collaborative learning process. The tool can be installed as a module in Moodle or in stand-alone mode -
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Catalog software for a personal library. Integrates with ISBNdb for lookup of metadata. Data-driven application using SWT and JFace Data Binding; EJB3-based data access layer to allow alternative servlet implementation.
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Departements_Francais
Logiciel d'apprentissage des départements français
Logiciel ludique d'apprentissage des départements français, avec quizz pour le contrôle des connaissances. -
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Dependency Discovery Tool
A tool for discovering external dependencies in Office files.
The Dependency Discovery Tool searches through binary office files (.doc, .xls and .ppt) and tries to find any documents or files that are linked to the document. It is written in java, using the Apache POI libraries ( http://poi.apache.org ) This project was part of a summer scholarship from the School of Engineering and Computer Science at Victoria University, Wellington (http://ecs.vuw.ac.nz) in conjunction with Archives New Zealand ( http://archives.govt.nz ). At the moment it requires Java 6 to build and run, but this should change soon, and it will run on Java 5 upwards. -
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Customizable and cross-platform system for distributed management of local sports facilities, with a client-server model.
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DES is mainly focused on managing university type exams containing descriptive questions or a mix of descriptive and objective type questions. Various exam related tasks are made easy and online using the tool. Please visit www.cdacmumbai.in/des
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A platform independent, versatile and purely offline DESKTop dictIONARY. Feature of platform independence makes it a unique dictionary which can be run on almost every operating systems. Sounds for pronounciation of words may be added in future.
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Desktop Dictionary is an online based desktop application which can find English word meanings from different online dictionary web sites and save them to local drive.
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The Desktop Environment For Kids is a kid friendly environment for UNIX/Linux, complete with kid oriented desktop themes and desk accesories.
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DetectorCalibration
Peak and global efficiency of radiation source-detector equipment
This software uses Geant4 simulation toolkit for computing detector efficiency in terms of peak efficiency and gross efficiency. It is designed mainly for gamma detectors (NaI or HpGe) but can be extended for beta or any other radiation type. Radiation source can be frontal beam, point source, Sarpagan baker (cylinder) and Marinelli baker. Due to the nature of Monte Carlo simulation technique, all geometries involved must be well known for accurate results! Can be used along with RadiationHelper (Beta_Gamma_MC module with Geant4 checkbox selected) but make sure that inside RadiationHelper folder there is MonteCarloPath.txt file having the first line pointing to the path of detector.exe folder (e.g. D:\\DetectorCalibration\\Build) and, if on Linux, next lines pointing to the Geant4 data. RadiationHelper is found here: https://sourceforge.net/projects/radiationhelper/ Source code included. Also, available on GitHub: https://github.com/ZiliasTheSaint/DetectorCalibration.git -
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Developer Roadmap Chinese
The roadmap to becoming a web developer in 2021
In this project, you will see a series of charts showing the paths you can take and the technologies you will want to adopt in order to become a front-end, back-end, or DevOps developer. I made these charts for an old professor of mine. He wanted to share something with college students and give them a point of view; share them here to help the community. The purpose of these roadmaps is to give you an outline and to provide you with guidance when you are confused about what to learn next, not to encourage you to learn what is trendy and popular. You should have a better understanding of why a certain tool is more suitable for use in certain situations than others, and remember that the trend and popularity never mean that it is the most suitable tool for the task. These roadmaps cover all the learning content of the path below. Don't feel overwhelmed. If you are just getting started, you don't need to learn everything from the beginning. -
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Developing Backbone.js Applications
A book on Backbone.js for beginners and advanced users alike
This is the home of Developing Backbone.js Applications, an open-source book about the Backbone.js library for structuring JavaScript applications. It is released under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 United States License and is currently available for early purchase via O'Reilly if you would like to support the project. We appreciate any and all efforts to help improve the book. Contains the source Markdown for each chapter. This is compiled into the HTML, ePub, Mobi and other formats found in the root of the project. Contains templates and metadata for compiling the book. Contains image assets for the project. Contains the source code for practicals. You can build the book using make or make -f Makefile. This will output the HTML, ePub, Mobi, PDF and RTF versions of the book.
