Search Results for "learn" - Page 25
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Showing 1944 open source projects for "learn"
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Ship Agents FasterGemini Enterprise Agent Platform lets you rapidly build, scale, govern and optimize production-ready agents grounded in your organization's data. The platform enables developers to build custom or pre-built agents for virtually any use case. New customers get $300 in free credits.
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Go kit
A standard library for microservices
...Use the databases, components, platform, and architecture that works best for you. Go is designed from first principles to advance the practice of software engineering. It's easy to learn, easy to master, and — most importantly — easy to maintain, by large and dynamic teams of engineers. -
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Gosu
2D game development library for Ruby and C++
...It provides a window and a main loop. 2D graphics and text, powered by OpenGL or OpenGL ES. Sounds and music, keyboard, mouse, and gamepad input. Gosu is mostly used to teach or learn Ruby or in short game development competitions. It’s also a great prototyping tool and should work for indie game development, though nobody has really tried that. In addition to the documentation linked from the sidebar, there is a free book by Mark Sobkowicz that will guide you through the creation of four different games. -
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Reaction Commerce
An API-first, modular commerce stack
...Its commerce capabilities and streaming microservices can be used together or individually, alongside or instead of your existing tech stack so you have full control and flexibility. It’s also highly extensible and customizable. Learn more about its beneficial business features here: https://reactioncommerce.com/ -
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Python Data Science Handbook
Python Data Science Handbook: full text in Jupyter Notebooks
The Python Data Science Handbook is a comprehensive collection of Jupyter notebooks written by Jake VanderPlas covering fundamental Python libraries for data science, including IPython, NumPy, Pandas, Matplotlib, Scikit-Learn and more. The project is designed for data scientists, researchers, and anyone transitioning into Python-based data work; it assumes you already know basic Python and focuses more on how to use the ecosystem effectively. Each chapter is a standalone Jupyter notebook, with runnable code, explanatory prose, visuals, and examples showing how to handle data-wrangling, exploratory data analysis, machine learning workflows, and visualization. ... -
Fully Managed MySQL, PostgreSQL, and SQL ServerCloud SQL handles your database ops end to end, so you can focus on your app.
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TensorFlow Documentation
TensorFlow documentation
An end-to-end platform for machine learning. TensorFlow makes it easy to create ML models that can run in any environment. Learn how to use the intuitive APIs through interactive code samples. -
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javaboy-code-samples
Collection of Java code examples and demo projects
...Each sample serves as a compact learning unit for a specific technology or pattern, allowing developers to explore one concern at a time without navigating a massive codebase. The repository often accompanies blog posts and tutorials, helping developers learn by reading articles and examining the corresponding code side-by-side. Because these practical examples cover common real-world problems and solutions, the project has amassed significant developer interest and engagement. -
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haoel.github.io
Comprehensive guide on self-hosting proxies, VPNs and transparent gate
This repository is a hands-on, longform guide for building and operating your own “scientific” Internet access setup — i.e., self-hosted proxies, VPNs, and transparent gateways — so you can reach blocked resources, protect privacy, and learn the underlying networking principles. It walks through buying and evaluating VPSes (regions, bandwidth, CN2/GIA lines), enabling kernel/network optimizations (BBR), and applying production practices like TLS certificates with Let’s Encrypt and using Cloudflare safely. The guide provides multiple deployment patterns (Dockerized gost / Shadowsocks / L2TP-IPSec, IKEv2 suggestions, and Cloudflare WARP integration), plus client configuration instructions for desktop and mobile (Clash, SwitchyOmega, Shadowrocket, etc.). ... -
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ChatGPT Plugins Collection
An unofficial collection of Plugins for ChatGPT
ChatGPT-Plugins-Collection is a community-driven repository that gathers examples and resources for building, testing, and experimenting with ChatGPT plugins. The collection provides a variety of plugin implementations that showcase different use cases, helping developers learn how to extend ChatGPT’s functionality. It is designed to serve both as a learning resource for beginners and a reference point for more experienced developers. By centralizing community contributions, the repository highlights practical applications of plugins across domains such as productivity, data access, and automation. The project also serves as a starting point for developers interested in building their own custom plugins, offering inspiration and code samples. ... -
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InfyOm Laravel Generator
API and Admin Panel CRUD Generator for Laravel.
We love open-source technologies and believe in contributing back to the community. With our proven expertise, our packages are used by thousands of developers every month. Generator Builder is an add-on package for Laravel generator which helps the developers to create CRUD and APIs via a visual interface. Swagger Generator package is an add-on package of Laravel generator which helps you to generate documentation of your APIs. -
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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sense2vec
Contextually-keyed word vectors
sense2vec (Trask et. al, 2015) is a nice twist on word2vec that lets you learn more interesting and detailed word vectors. This library is a simple Python implementation for loading, querying and training sense2vec models. For more details, check out our blog post. To explore the semantic similarities across all Reddit comments of 2015 and 2019, see the interactive demo. -
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bugstack
Java Backend Development Skills Roadbook
...It separates core technologies from supporting routes so learners can prioritize what helps them become productive first. The project is useful for students, junior developers, and working programmers who need a clearer view of what to learn next. It functions as both a structured checklist and a navigation map for Java backend career growth. -
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ScikitLearn.jl
Julia implementation of the scikit-learn API
The scikit-learn Python library has proven very popular with machine learning researchers and data scientists in the last five years. It provides a uniform interface for training and using models, as well as a set of tools for chaining (pipelines), evaluating, and tuning model hyperparameters. ScikitLearn.jl brings these capabilities to Julia. Its primary goal is to integrate both Julia- and Python-defined models together into the scikit-learn framework. -
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KAGSA
KAGSA PROGRAMMING LANGUAGE
it designed to be easy to learn and practical for use in various projects. It has a flexible syntax and allows some things that other languages prohibit, such as using certain symbols in variable names and starting variable names with numbers. KAGSA contains several main components, including a lexer, syntax checker, parser, and compiler. It supports object-oriented programming, iteration, and other features commonly used in programming languages. -
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AurelEdit IDE
Coding IDE for Basic programming
...AurelEdit has syntax highlighting and theme changer. The program is portable application and don't have any impact on regisrty, works out of the box and is great for beginners or hobbyist to learn and use BASIC language. In AurelEdit you can easily develop and execute programs, written in BASIC languages. Enter code in editor and simply compile & run your program. In Windows can execute programs in built-in Oxygen Basic and in micro(A) Interpreter. -
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T81 558
Applications of Deep Neural Networks
...Through a combination of advanced training techniques and neural network architectural components, it is now possible to create neural networks that can handle tabular data, images, text, and audio as both input and output. Deep learning allows a neural network to learn hierarchies of information in a way that is like the function of the human brain. This course will introduce the student to classic neural network structures, Convolution Neural Networks (CNN), Long Short-Term Memory (LSTM), Gated Recurrent Neural Networks (GRU), General Adversarial Networks (GAN) and reinforcement learning. Application of these architectures to computer vision, time series, security, natural language processing (NLP), and data generation will be covered. ... -
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auto-sklearn
Automated machine learning with scikit-learn
auto-sklearn is an automated machine learning toolkit and a drop-in replacement for a scikit-learn estimator. auto-sklearn frees a machine learning user from algorithm selection and hyperparameter tuning. It leverages recent advantages in Bayesian optimization, meta-learning and ensemble construction. Auto-sklearn 2.0 includes latest research on automatically configuring the AutoML system itself and contains a multitude of improvements which speed up the fitting the AutoML system. auto-sklearn 2.0 works the same way as regular auto-sklearn. auto-sklearn is licensed the same way as scikit-learn, namely the 3-clause BSD license. -
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fre:ac
The fre:ac audio converter project
fre:ac is a free audio converter and CD ripper with support for various popular formats and encoders. It converts freely between MP3, M4A/AAC, FLAC, WMA, Opus, Ogg Vorbis, Speex, Monkey's Audio (APE), WavPack, WAV and other formats. With fre:ac you easily rip your audio CDs to MP3 or M4A files for use with your hardware player or convert files that do not play with other audio software. You can even convert whole music libraries retaining the folder and filename structure. The integrated CD... -
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Aniseed
Neovim configuration and plugins in Fennel (Lisp compiled to Lua)
...It’ll allow you to send portions of your code off for evaluation as well as see the results in an interactive log buffer. Aniseed ships with a set of module macros that make interactive evaluation not only possible but rich and intuitive. You should read:h aniseed to learn the details but it’s worth mentioning that you opt-in by starting your file with a (module …) block, you then export values from your module with the (def…) macros. -
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FireWeb
Java Single Page Web Application Framework
Java Single Page Application Framework. Using POJO/JavaScript/WebSocket/HTML. Without any scripting - only pure Java classes. Simply and easy to learn, use and extend. -
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Bayesian Methods for Hackers
An introduction to Bayesian methods + probabilistic programming
...It includes notebook-based chapters that let learners run and modify examples directly. The material is especially useful for programmers, data scientists, and technically curious readers who want to learn Bayesian methods through code. It remains a widely referenced entry point for making Bayesian statistics feel practical and approachable. -
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Self-learning-Computer-Science
Resources to learn computer science in your spare time
Self-learning Computer Science is a curated, open-source guide repository designed to help learners independently study computer science topics using high-quality university-level resources. The author (an undergraduate CS student) assembled links to courses from institutions like MIT, UC Berkeley, Stanford, etc., covering mathematics, programming, data structures/algorithms, computer architecture, machine learning, software engineering and more. It’s aimed at learners who find traditional... -
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Mars Framework
Mars is a tensor-based unified framework for large-scale data
Mars is a distributed computing framework designed to scale scientific computing and data science workloads across large clusters while preserving the familiar programming interfaces of common Python libraries. The project provides a tensor-based execution model that extends the capabilities of tools such as NumPy, pandas, and scikit-learn so that large datasets can be processed in parallel without rewriting code for distributed environments. Its architecture automatically divides large computational tasks into smaller chunks that can be executed across multiple nodes in a cluster, allowing complex analytics, machine learning workflows, and data transformations to run efficiently at scale. ... -
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SpecTcl is a Tcl/Tk based histogrammer suitable for analysis of nuclear physics data. SpecTcl is relatively easy to learn, and is based on top of a very open C++ histogramming class framework. SpecTcl has been developed under NSF grant PHY-9528844 and DOE grant DE-SC0000661 Note that as of July 2023, all future development and release packages will be occur at https://github.com/FRIBDAQ/SpecTcl
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Dyalect
Dyalect is a programming language for .NET Core
...Dy runs on the top of its own high performance virtual machine. It compiles fast and can be used as an embeddable language or as a scripting language of your choice. It is also a good language to learn programming. Dyalect offers modern syntax, inspired by such languages as C#, Swift, Go and Rust, first class functions, coroutines, expressive modules, a dynamic type system with an ability to extend existing types with new functions and much more. Dy is shipped with a crossplatform interactive console. Main repo: https://github.com/vorov2/dyalect -
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learn-machine-learning-in-two-months
Essential Knowledge for learning Machine Learning in two months
The learn-machine-learning-in-two-months repository is an educational open-source project designed to guide beginners through the process of learning machine learning and deep learning concepts within a structured two-month study plan. The project compiles curated resources, tutorials, and practical notebooks that introduce fundamental topics such as mathematics for machine learning, Python programming, and essential libraries like NumPy and TensorFlow.