Search Results for "learning" - Page 33
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Showing 4819 open source projects for "learning"
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free-code
The free build of Claude Code
Free-code is an open-source platform aimed at providing accessible coding resources, tools, or templates that help developers learn, build, and share projects without barriers. It typically focuses on simplifying the development process by offering prebuilt components, example projects, or utilities that can be reused across different applications. The project is designed to encourage collaboration and knowledge sharing within the developer community, making it easier for beginners and... -
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PaddleOCR-json
OCR offline image text recognition command line windows program
...It wraps the PaddleOCR models, which are capable of detecting and recognizing text in a wide variety of languages and layouts, into a self-contained executable that can be run locally without needing a deep learning environment configured manually. This makes it practical for developers or system integrators who want reliable OCR output in JSON while avoiding the complexity of training or managing models by hand. Projects and wrappers built around PaddleOCR-json demonstrate how it can be integrated into other applications, such as desktop OCR utilities or language-specific bindings, because the JSON output is easy to parse and consume. -
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FISSURE
The RF and reverse engineering framework for everyone
FISSURE is an open-source radio frequency analysis and signal intelligence framework built to support software-defined radio research, wireless security experimentation, and protocol reverse engineering. The project brings together tools for capturing, inspecting, decoding, replaying, and analyzing RF signals across a wide range of wireless technologies. It is designed as a practical environment for researchers and operators who need to move from raw spectrum observation to structured... -
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Synthetic Data Generator
SDG is a specialized framework
...The platform enables developers and data scientists to create artificial datasets that preserve important relationships between variables without containing sensitive personal information. This makes the generated data suitable for tasks such as machine learning model training, testing software systems, sharing datasets across organizations, and conducting research without violating privacy regulations. The system supports multiple generation methods including statistical models, generative adversarial networks, and large language model–based synthesis. It also includes a data processing module capable of handling different data types, preprocessing columns, managing missing values, and converting formats automatically before model training. -
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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Xtuner
A Next-Generation Training Engine Built for Ultra-Large MoE Models
...Its architecture incorporates memory-efficient optimizations that allow researchers to train large models even when computational resources are limited. XTuner is also designed to integrate with modern AI ecosystems, supporting multimodal training, reinforcement learning optimization, and instruction tuning pipelines. -
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AppAgent
Multimodal Agents as Smartphone Users, an LLM-based multimodal agent
...AppAgent combines vision capabilities with language reasoning to understand interface elements and determine which actions are required to accomplish a task. The system also includes mechanisms for exploration and learning, allowing the agent to analyze user interface layouts and build structured knowledge about how different apps function. -
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AI Data Science Team
An AI-powered data science team of agents
AI Data Science Team is a Python library and agent ecosystem designed to accelerate and automate common data science workflows by modeling them as specialized AI “agents” that can be orchestrated to perform tasks like data cleaning, transformation, analysis, visualization, and machine learning. It provides a modular agent framework where each agent focuses on a step in the typical data science pipeline — for example, loading data from CSV/Excel files, cleaning and wrangling messy datasets, engineering predictive features, building models with AutoML, connecting to SQL databases, and producing visual outputs — all driven by natural language or programmatic instructions. ... -
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StatsForecast
Fast forecasting with statistical and econometric models
StatsForecast is a Python library for time-series forecasting that delivers a suite of classical statistical and econometric forecasting models optimized for high performance and scalability. It is designed not just for academic experiments but for production-level time-series forecasting, meaning it handles forecasting for many series at once, efficiently, reliably, and with minimal overhead. The library implements a broad set of models, including AutoARIMA, ETS, CES, Theta, plus a battery... -
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marimo
A reactive notebook for Python
marimo is an open-source reactive notebook for Python, reproducible, git-friendly, executable as a script, and shareable as an app. marimo notebooks are reproducible, extremely interactive, designed for collaboration (git-friendly!), deployable as scripts or apps, and fit for modern Pythonista. Run one cell and marimo reacts by automatically running affected cells, eliminating the error-prone chore of managing the notebook state. marimo's reactive UI elements, like data frame GUIs and plots,... -
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LossFunctions.jl
Julia package of loss functions for machine learning
...As such, it is a part of the JuliaML ecosystem. The sole purpose of this package is to provide an efficient and extensible implementation of various loss functions used throughout Machine Learning (ML). It is thus intended to serve as a special purpose back-end for other ML libraries that require losses to accomplish their tasks. To that end we provide a considerable amount of carefully implemented loss functions, as well as an API to query their properties (e.g. convexity). Furthermore, we expose methods to compute their values, derivatives, and second derivatives for single observations as well as arbitrarily sized arrays of observations. ... -
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PySR
High-Performance Symbolic Regression in Python and Julia
PySR is an open-source tool for Symbolic Regression: a machine learning task where the goal is to find an interpretable symbolic expression that optimizes some objective. Over a period of several years, PySR has been engineered from the ground up to be (1) as high-performance as possible, (2) as configurable as possible, and (3) easy to use. PySR is developed alongside the Julia library SymbolicRegression.jl, which forms the powerful search engine of PySR. -
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Ct.js
Ct.js is a desktop game engine that makes learning programming fun
ct.js makes learning programming fun and game development easy by its visual tools, good docs and flexible, modular library. It is free, open-source, and is loved by hobbyists, professionals, teachers, and their students. Bad tools hinder your performance. Ct.js is designed to be like a brush with which you create games, not to be an enemy you will fight with. ct.js bundles come with offline docs, tutorials, and editable examples and demos. -
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MagicTools
A list of Game Development resources to make magic happen
...It also includes game engines, frameworks, artificial intelligence tools, audio libraries, music editors, and board game resources. Separate sections point developers toward books, blogs, podcasts, game jams, communities, project management tools, complete source projects, and learning materials. Clear license markers distinguish free, open-source, paid, and partially free offerings. The repository is useful for independent developers and teams assembling a practical game development toolkit. -
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pytudes
Python programs, usually short, of considerable difficulty
...The repository includes readable solutions, experiments, notebooks, and scripts that cover algorithms, puzzles, probability, search, language processing, simulation, and mathematical reasoning. It is useful for programmers who want to study elegant Python code while learning how experienced developers approach problem solving. Many examples emphasize clarity and compactness rather than framework-heavy engineering. pytudes is best understood as a learning library, a coding style reference, and a set of practical programming studies. -
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OpenGame
Open Agentic Coding for Games
...The project likely supports experimentation with different gameplay mechanics and encourages customization through open-source contributions. Its design philosophy emphasizes ease of use while still allowing developers to scale complexity as needed. OpenGame may also serve as a learning resource for beginners exploring game development concepts. Overall, it represents a lightweight, extensible approach to building games without relying on heavy commercial engines. -
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The Open Source Computer Science Degree
Video discussing this curriculum
The Open Source Computer Science Degree is a curated collection of free resources designed to help individuals learn computer science concepts without formal education. It aggregates courses, books, tutorials, and tools covering topics such as algorithms, data structures, systems design, and programming languages. The repository is organized to guide learners through a structured path similar to a university curriculum. It emphasizes accessibility, allowing anyone to gain a comprehensive CS... -
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NFH Self-Improvement Loop
Minimal adversarial framework for AI agent self-modification
...It focuses on creating feedback loops where outputs are evaluated, refined, and reintroduced into the system for further improvement. The project emphasizes iterative learning, allowing systems to evolve over time through repeated evaluation and adjustment. It can be applied to areas such as content generation, decision-making, and personal productivity systems. The framework encourages structured reflection and optimization, ensuring that each iteration builds upon previous results. It is particularly useful for experimenting with autonomous improvement processes in AI workflows. ... -
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TurboQuant+
Implementation of TurboQuant (ICLR 2026)
...The project explores additional enhancements such as improved calibration, adaptive quantization, and potentially hybrid precision approaches that combine multiple levels of compression. It is designed to be used in conjunction with modern machine learning workflows, particularly those involving large models that require optimization for deployment. TurboQuant Plus focuses on experimentation and performance tuning, allowing developers to test different configurations and evaluate trade-offs. Its architecture supports extensibility, enabling further development of quantization methods and integration with existing ML pipelines. -
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Top-AI-Conferences-Paper-with-Code
This repository is a collection of AI top conferences papers
...The repository organizes papers by conference and year, providing a structured overview of developments across natural language processing, computer vision, machine learning, and other AI fields. -
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Finance
150+ quantitative finance Python programs
Finance is a repository that compiles structured notes and educational material related to financial analysis, markets, and quantitative finance concepts. The project focuses on explaining key principles used in finance and investment analysis, including topics such as financial statements, valuation models, portfolio theory, and financial markets. The repository is designed as a study reference for students and professionals who want to understand financial systems and the analytical... -
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Data Science Articles from CodeCut
Collection of useful data science topics along with articles
The Data-science repository from CodeCutTech is a curated collection of educational content focused on practical tools and workflows used in modern data science projects. Instead of providing a single software package, the repository aggregates articles, tutorials, and examples covering many topics within the data science ecosystem. The materials address areas such as MLOps, data management, project organization, testing practices, visualization techniques, and productivity tools used by... -
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Fish Skin AI Knowledge Base
Programmer Fish Skin's AI Resource Guide
Fish Skin AI Knowledge Base is a comprehensive open knowledge base and tutorial collection that helps developers quickly learn, evaluate, and apply modern AI technologies, especially in the context of “vibe coding” and practical AI product development. The project curates structured learning paths covering model selection, AI coding tools, agent platforms, prompt engineering, and full-stack AI application workflows. It combines beginner-friendly introductions with advanced guides on context management, hallucination mitigation, and production-quality code practices. The repository also includes hands-on project tutorials that walk users from zero to deployable AI products across multiple application types. ... -
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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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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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PRML
PRML algorithms implemented in Python
PRML repository is a respected and well-maintained project that implements the foundational algorithms from the famous textbook Pattern Recognition and Machine Learning by Christopher M. Bishop, providing a practical and accessible Python reference for both students and professionals. Rather than just summarizing concepts, the repository includes working code that demonstrates linear regression and classification, kernel methods, neural networks, graphical models, mixture models with EM algorithms, approximate inference, and sequential data methods — all following the book’s structure and notation. ...