Search Results for "language learning" - Page 9
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Showing 934 open source projects for "language learning"
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Go by Example
1000+ Hand-Crafted Go Examples, Exercises, and Quizzes
Go by Example is a large hands-on curriculum for learning the Go programming language through examples, exercises, quizzes, and small projects. It contains more than a thousand hand-crafted programs intended to be fixed, completed, or studied directly. Lessons progress from installation and first programs through variables, control flow, arrays, slices, strings, maps, structs, functions, pointers, interfaces, and concurrency. -
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cmdh
Create Linux commands from natural language, in the shell
...It is particularly useful for beginners learning shell environments as well as experienced users who want to speed up repetitive tasks. cmdh emphasizes minimalism and speed, offering a lightweight interface that fits seamlessly into existing command-line workflows. It can be used interactively or integrated into scripts, making it flexible for different use cases. Overall, cmdh enhances productivity by bridging the gap between natural language and technical command execution. -
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Chinese-XLNet
Chinese XLNet pre-trained model
Chinese-XLNet is a Chinese language pre-trained model based on the XLNet architecture, providing an advanced foundation for natural language processing tasks in Mandarin and other Chinese dialects. Unlike traditional masked language modeling, XLNet uses a permutation language modeling objective that captures bidirectional context more effectively by training over all possible token orderings, yielding richer contextual representations. -
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BigMac
An open-source toolkit for BigMac-style pipeline-parallel training
BigMac is an open-source toolkit for pipeline-parallel training of multimodal large language models. It preserves optimized language-model pipeline schedules while placing encoder and generator work around them. This design reduces activation memory without bringing back cross-module pipeline bubbles. Its scheduler creates global operator plans, while its executor runs those plans through a shared schedule abstraction. A Megatron-Core reference backend and Qwen3 and Qwen3-VL tutorials help... -
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OpenJarvis
Personal AI, On Personal Devices
...The framework provides shared primitives for building local-first agents, along with evaluation tools that measure performance using metrics such as energy consumption, latency, cost, and accuracy. OpenJarvis integrates with local inference engines like Ollama, vLLM, SGLang, and llama.cpp to run language models directly on personal hardware. It also includes a learning loop that allows models to improve over time using locally generated interaction traces. By prioritizing local execution and efficiency, OpenJarvis aims to provide a foundation for privacy-preserving personal AI assistants. -
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xLSTM
Neural Network architecture based on ideas of the original LSTM
xLSTM is an open-source machine learning architecture that reimagines the classic Long Short-Term Memory (LSTM) network for modern large-scale language modeling and sequence processing tasks. The project introduces a new recurrent neural network design that incorporates exponential gating mechanisms and enhanced memory structures to overcome limitations of traditional LSTM models. -
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Rust Course
It has been the world's most popular language for 8 consecutive years
The Rust语言圣经 (Rust Language Bible) is a comprehensive, Chinese-language Rust tutorial that aims to take learners from beginner concepts to advanced mastery. The course is carefully designed with a structured catalog, vivid and approachable language, and an engaging style that avoids the dry and mechanical tone of many technical books. It covers the basics of Rust, such as ownership, borrowing, lifetimes, traits, and generics, but also dives deep into advanced topics like performance optimization, linked list implementations, async programming with Tokio, standard library internals, Cargo usage, and WebAssembly development. ... -
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rust-bert
Rust native ready-to-use NLP pipelines and transformer-based models
rust-bert is a Rust-based implementation of transformer-based natural language processing models that provides ready-to-use pipelines for tasks such as text classification, summarization, and question answering. The project ports many capabilities of the Hugging Face Transformers ecosystem into the Rust programming language. It allows developers to run state-of-the-art NLP models like BERT, GPT-2, and DistilBERT directly within Rust applications while maintaining high performance and memory efficiency. ... -
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Happy-LLM
Large Language Model Principles and Practice Tutorial from Scratch
Happy-LLM is an open-source educational project created by the Datawhale AI community that provides a structured and comprehensive tutorial for understanding and building large language models from scratch. The project guides learners through the entire conceptual and practical pipeline of modern LLM development, starting with foundational natural language processing concepts and gradually progressing to advanced architectures and training techniques. It explains the Transformer... -
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GSY Flutter Book
Flutter complete development practical explanation series
GSY Flutter Book is an open-source book (or tutorial style project) built for learning Flutter (the cross-platform mobile UI toolkit from Google) and other related mobile UI development topics. It uses the Flutter framework (Dart language) and provides practical examples, code snippets, screenshots, and detailed explanations of UI components, state management, navigation, animations, architecture, etc. The structure of the book is such that you’ll get both theoretical explanation and hands-on code you can run and adapt. ... -
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MetaScreener
AI-powered tool for efficient abstract and PDF screening
...The system helps researchers analyze large collections of academic abstracts and research papers to determine which studies are relevant for inclusion in evidence synthesis projects. Instead of manually reviewing hundreds or thousands of documents, researchers can use MetaScreener to apply machine learning techniques that assist with classification and prioritization of candidate papers. The platform can analyze both abstracts and full PDF documents, enabling automated filtering based on research criteria defined by the user. By incorporating natural language processing techniques, the system can identify potentially relevant studies and reduce the workload associated with manual screening. -
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The Algorithms - C++ #
Collection of various algorithms in mathematics, machine learning
TheAlgorithms/C-Plus-Plus is a large open-source repository that collects implementations of many classic algorithms and data structures written in the C++ programming language. The project is part of the broader “The Algorithms” initiative, which maintains algorithm implementations in several programming languages to support education and knowledge sharing. Within the C++ repository, contributors implement algorithms across a wide range of fields including sorting, graph theory, number theory, machine learning, cryptography, and data structures. ... -
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VibeTensor
Our first fully AI generated deep learning system
...What makes VibeTensor remarkable is that every major component, from core libraries and dispatch systems to CUDA runtime support, caching allocators, and language bindings, was created and validated by coding agents using automated builds and tests rather than manual line-by-line human coding. The system includes both a Python frontend via a torch-like API and an experimental Node.js/TypeScript interface. -
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Understand Anything
Turn any codebase into an interactive knowledge graph
Understand-Anything is an AI-driven tool designed to help users deeply understand any topic by generating structured explanations, summaries, and breakdowns. It focuses on transforming complex or unfamiliar subjects into clear, step-by-step explanations that are easier to grasp. The system leverages language models to provide layered insights, allowing users to explore topics at different levels of detail. It is particularly useful for learning, research, and quick comprehension of new concepts across various domains. The project emphasizes accessibility, making advanced knowledge more approachable for a wider audience. It also supports iterative questioning, enabling users to refine their understanding through follow-up queries. -
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33 JS Concepts
33 JavaScript concepts every developer should know
33-js-concepts is a curated collection of essential JavaScript concepts that every developer should understand to strengthen their knowledge of the language. The project was originally inspired by an article by Stephen Curtis and has since grown into a community-driven learning resource. It serves as a roadmap for developers to review and master core principles such as closures, promises, prototypes, event loops, and other critical topics. While not a strict curriculum, it provides a structured path for deeper study and practical learning. ... -
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Agentic RAG for Dummies
A modular Agentic RAG built with LangGraph
Agentic RAG for Dummies is an educational repository that demonstrates how to build retrieval-augmented generation systems combined with autonomous AI agents. The project explains the principles behind agentic retrieval pipelines where language models can dynamically decide when to retrieve information, analyze results, and plan further actions. Instead of relying on static retrieval pipelines, the system shows how agents can orchestrate retrieval, reasoning, and tool usage in a more... -
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AI CODE GUIDE
AI Code Guide is a roadmap to start coding with AI
AI CODE GUIDE is an open educational repository that provides a comprehensive roadmap for learning how to use artificial intelligence tools for software development. The project organizes resources related to AI-assisted programming, including tutorials, research papers, developer tools, and practical guidelines. Its goal is to help both experienced programmers and beginners understand how AI models can assist with writing, reviewing, and designing software. The repository discusses emerging... -
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LiteRT
LiteRT, successor to TensorFlow Lite
LiteRT is Google's next-generation on-device machine learning framework and the successor to TensorFlow Lite, designed for high-performance AI and generative AI deployment across edge devices. It provides efficient model conversion, optimization, and runtime execution while leveraging hardware acceleration from CPUs, GPUs, and NPUs. LiteRT supports a wide range of platforms, including Android, iOS, Linux, macOS, Windows, web environments, and IoT devices. -
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Bespoke Curator
Synthetic data curation for post-training and data extraction
Curator is an open-source Python library designed to build synthetic data pipelines for training and evaluating machine learning models, particularly large language models. The system helps developers generate, transform, and curate high-quality datasets by combining automated generation with structured validation and filtering. It supports workflows where models are used to produce synthetic examples that can later be refined into reliable training datasets for reasoning, question answering, or structured information extraction tasks. ... -
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Anki
Anki is a smart spaced repetition flashcard program
...It supports a wide variety of media types (text, images, audio, LaTeX), advanced scheduling algorithms (SM‑2, FSRS), and extensibility via add‑ons. It’s widely used for education, language learning, medical training, and more. -
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Tongyi DeepResearch
Tongyi Deep Research, the Leading Open-source Deep Research Agent
DeepResearch (Tongyi DeepResearch) is an open-source “deep research agent” developed by Alibaba’s Tongyi Lab designed for long-horizon, information-seeking tasks. It’s built to act like a research agent: synthesizing, reasoning, retrieving information via the web and documents, and backing its outputs with evidence. The model is about 30.5 billion parameters in size, though at any given token only ~3.3B parameters are active. It uses a mix of synthetic data generation, fine-tuning and... -
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AI Engineering Hub
In-depth tutorials on LLMs, RAGs and real-world AI agent applications
The AI Engineering Hub repository is a large open-source collection of hands-on projects, tutorials, and real-world AI engineering resources designed to help developers learn and build with modern AI technologies, especially large language models (LLMs), retrieval-augmented generation (RAG), and agent-based systems. It includes more than 90 production-ready projects across skill levels, organized into beginner, intermediate, and advanced categories to guide users progressively from simple experiments to complex AI workflows. Projects range from OCR applications and local chatbot UIs to multimodal RAG systems and multi-agent automation pipelines, making the hub valuable both as a learning resource and as a practical reference. ... -
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AI Notes
Curated AI engineering notes on LLMs, generative models, and tools
...These notes include observations, references, experiments, and summaries of important research and industry developments in AI. ai-notes also contains collections of prompts, curated learning materials, and categorized resources intended to help developers explore AI capabilities and practical applications. -
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MLX Engine
LM Studio Apple MLX engine
MLX Engine is the Apple MLX-based inference backend used by LM Studio to run large language models efficiently on Apple Silicon hardware. Built on top of the mlx-lm and mlx-vlm ecosystems, the engine provides a unified architecture capable of supporting both text-only and multimodal models. Its design focuses on high-performance on-device inference, leveraging Apple’s MLX stack to accelerate computation on M-series chips. The project introduces modular VisionAddOn components that allow image... -
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WeClone
One-stop solution for creating your digital avatar from chat history
WeClone is an open source AI project designed to replicate a person’s conversational style and personality by training models on chat history data. The system analyzes message patterns, linguistic style, and contextual behavior in order to generate responses that resemble the original user’s communication style. It is intended primarily as an experimental exploration of digital personality modeling and conversational AI personalization. By processing large volumes of conversation data,...
