Search Results for "programming learning system" - Page 19
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Showing 1087 open source projects for "programming learning system"
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Semantic
Parsing, analyzing, and comparing source code across many languages
semantic is a Haskell library and command line tool for parsing, analyzing, and comparing source code. Run semantic --help for complete list of up-to-date options. Semantic uses tree-sitter to generate parse trees, but layers in a more generalized notion of syntax terms across all supported programming languages. We'll see why this is important when we get to diffs and program analysis, but for now let's just inspect some output. It helps to have a simple program to parse. Symbols are named... -
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pytorch-examples
Simple examples to introduce PyTorch
...By emphasizing readable code, the repository helps users understand how PyTorch’s imperative programming style enables flexible model development. It also serves as a quick reference for common patterns and techniques used in deep learning workflows. The project aligns with PyTorch’s philosophy of combining usability with performance and flexibility. Overall, pytorch-examples is an essential learning resource for anyone working with PyTorch. -
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php login with email validation
Complete PHP Login with Email Validation System
This is the only complete php login system developed in core php. The system has many features that enable you to learn php Object Oriented Programming (OOP). This program is also very useful for those that want to learn how to create a complete login system in php. INSTALLATION AND SETUP PROCEDURE: https://www.youtube.com/channel/UCBemL4ht7jmZY9tJ8NGkS-A?sub_confirmation=1 subscribe to our youtube channels for more source codes. -
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Active Learning
Framework and examples for active learning with machine learning model
Active Learning is a Python-based research framework developed by Google for experimenting with and benchmarking various active learning algorithms. It provides modular tools for running reproducible experiments across different datasets, sampling strategies, and machine learning models. The system allows researchers to study how models can improve labeling efficiency by selectively querying the most informative data points rather than relying on uniformly sampled training sets. ... -
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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Facets
Visualizations for machine learning datasets
The power of machine learning comes from its ability to learn patterns from large amounts of data. Understanding your data is critical to building a powerful machine learning system. Facets contains two robust visualizations to aid in understanding and analyzing machine learning datasets. Get a sense of the shape of each feature of your dataset using Facets Overview, or explore individual observations using Facets Dive. -
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Didact
A DIY guide to build your own React
Didact is an educational JavaScript project that teaches developers how to build a small React-like library from scratch. It accompanies a series of explanatory posts that break down React concepts step by step. The project covers rendering DOM elements, element creation, JSX, virtual DOM behavior, reconciliation, components, state, Fiber-style incremental reconciliation, and hooks. Its goal is not to replace React, but to make React’s internal ideas easier to understand through a compact... -
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nuBASIC
BASIC language interpreter (multiplatform)
nuBASIC is an implementation of an interpreter of the BASIC programming language and IDE. It has been designed mainly for educational purposes and it is suitable for simple games, educational or small business programs. -
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Java Simple Codes
Simple codes in Java for beginners
This repository provides a set of simple codes to start learning programming. It aims to help new learner to familiarize with basic programming notions and simple algorithms which must be known by every programmer. Every notion or idea is presented in a separate class while the test class provides an example on how to call (or use) this notion. In a first time, no OO programming notions are introduced. -
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NoderCMS
Node.js + MongoDB
Super concurrency and super high data reading speed, coupled with flexible custom models, easy-to-use templates, advanced background management system and multi-user permissions, have created a perfect NoderCMS. The inherent asynchronous non-blocking programming brought by Node.js makes NoderCMS calmly face up to millions of concurrent visits. Thanks to the non-relational database MongoDB, everything is stored in BSON format, faster and faster. Through the combination of models and custom fields, you can easily create completely different columns and various recommendation positions according to your needs. ... -
Train ML Models With SQL You Already KnowBuild and deploy ML models using familiar SQL. Automate data prep with built-in Gemini. Query 1 TB and store 10 GB free monthly.
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Pipelines
An experimental programming language for data flow
Pipelines is a language and runtime for crafting massively parallel pipelines. Unlike other languages for defining data flow, the Pipeline language requires the implementation of components to be defined separately in the Python scripting language. This allows the details of implementations to be separated from the structure of the pipeline while providing access to thousands of active libraries for machine learning, data analysis, and processing. -
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tinyraycaster
486 lines of C++: old-school FPS in a weekend
...The project focuses on teaching the principles behind rendering pseudo-3D scenes, including wall projection, texture mapping, and camera movement. Its simplicity allows developers to understand the entire rendering pipeline without the complexity of modern game engines. The codebase is structured as a learning resource, guiding users through the process of building a basic game engine from scratch. It is particularly useful for those interested in retro game development or low-level graphics programming. Overall, tinyraycaster offers a concise and accessible introduction to real-time rendering techniques used in early 3D games. -
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KAREL 3D WebGL
Children's learning programming language Karel the Robot in 3D
KAREL 3D written in JavaScript with WebGL is a children's programming language for teaching algorithms. This is later variant from Karel the Robot in 3D, created in 1986 for 8bit microcomputer PMD 85-2. Support full recursion, create new commands, complex commands with condition from dialog and direct control with keys, language translation. Objects: brick, wall, mark, fence. Check out the Wiki (here) for more examples and pictures. Include ZIP with original historical... -
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RaptorJIT
A dynamic language for system programming (LuaJIT fork)
RaptorJIT is a Lua implementation suitable for high-performance low-level system programming. If you want to use a simple dynamic language to write a network stack; a hypervisor; a unikernel; a database; etc, then you have come to the right place. -
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DCLPS
DCLPS: database control loading and prompting system
FRESHHH DCLPS can be considered an object relational mapping system ORM framework. It is robust well structured web oriented programming system that allows to create web systems (web sites) just by coding javascript. That is possible cause it uses a few server php script files that are requested by javascript to get some results. The key of this system is its structure that has to deal with nested tree objects and how it is implemented in the relational database. -
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C# Extension Methods
C# Extension Methods | Over 1000 extension methods:
...Open a binary file, reads the contents of the file into a byte array, and then closes the file. Extract all the files in the specified zip archive to a directory on the file system. Get started by learning C# Extension Methods from cheat sheet. Get started by searching from documented extension methods. Get started by searching and trying some online example. -
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Examples.CSharp
Shows how to use .Net Framework controls.
Examples CSharp shows how to use .Net Framework controls without RAD only by programming code (CSharp) * Github repository : https://github.com/gammasoft71/Examples.CSharp * Homepage : https://gammasoft71.wixsite.com/gammasoft/csharp -
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FastFlow: programming multi-core
Pattern-based multi/many-core parallel programming framework
FastFlow is a C/C++ programming framework supporting the development of pattern-based parallel programs on multi/many-core, GPUs and distributed platforms. FastFlow run-time is built upon non-blocking threads and lock-free queues. Thanks to its very efficient CAS-free communication/synchronization support (e.g. few clock cycles core-to-core latency), FastFlow effectively supports the exploitation of fine grain parallelism, e.g. parallel codes managing very high frequency streams on... -
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CFD Python
Sequence of Jupyter notebooks featuring the 12 Steps to Navier-Stokes
CFD Python, a.k.a. the 12 steps to Navier-Stokes, is a practical module for learning the foundations of Computational Fluid Dynamics (CFD) by coding solutions to the basic partial differential equations that describe the physics of fluid flow. The module was part of a course taught by Prof. Lorena Barba between 2009 and 2013 in the Mechanical Engineering department at Boston University (Prof. Barba since moved to George Washington University). -
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The Grid Programming Environment (GPE) turns a collection of computer systems into a Grid and provides graphical user interfaces and interoperable GridBeans for application development. It supports Globus Toolkit and Unicore-based infrastructures.
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tcomb
Type checking and DDD for JavaScript
...The library is useful in JavaScript projects where static type information is unavailable or insufficient at runtime. It can validate external inputs, document data expectations, and support safer programming patterns without requiring a full type system migration. tcomb also serves as the foundation for related projects such as tcomb-validation and tcomb-form. Its main value is bringing lightweight, expressive runtime type modeling to JavaScript applications. -
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Skylark
Skylark in Go: the Skylark configuration language
Skylark, now known as Starlark, is an interpreter for a Python-like language implemented in Go. It is designed as a lightweight, deterministic, and embeddable configuration and scripting language ideal for use within larger applications. Skylark maintains Python’s familiar syntax and high-level data types while omitting features that could cause nondeterminism, such as concurrency and dynamic module imports. The interpreter supports first-class functions, dictionaries, lists, and... -
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Skater
Python library for model interpretation/explanations
Skater is a unified framework to enable Model Interpretation for all forms of the model to help one build an Interpretable machine learning system often needed for real-world use-cases(** we are actively working towards to enabling faithful interpretability for all forms models). It is an open-source python library designed to demystify the learned structures of a black box model both globally(inference on the basis of a complete data set) and locally(inference about an individual prediction). ... -
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NimTorch
PyTorch - Python + Nim
NimTorch is a deep learning library for the Nim programming language, providing bindings to PyTorch for efficient tensor computations and neural network functionalities. -
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Mozart-Oz Programming System
Runtime and development environment of Oz
The Mozart Programming System is an open source implementation of the programming language Oz 3. Oz is a multi-paradigm language that supports declarative programming, object-oriented programming, constraint programming, concurrency and distributed programming as part of a coherent whole. -
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Functional, Data Science Intro To Python
[tutorial]A functional, Data Science focused introduction to Python
The first section is an intentionally brief, functional, data science-centric introduction to Python. The assumption is a someone with zero experience in programming can follow this tutorial and learn Python with the smallest amount of information possible. The sections after that, involve varying levels of difficulty and cover topics as diverse as Machine Learning, Linear Optimization, build systems, command line tools, recommendation engines, Sentiment Analysis and Cloud Computing.
