Research & Education

dive-into-llms is an open-source programming tutorial series designed to help users dive into large language models (LLMs). Originating from courses at Shanghai Jiao Tong University, this free resource offers practical programming exercises covering a wide range of LLM-related topics. Users can learn about fine-tuning and deploying pre-trained models, prompt engineering and chain-of-thought, knowledge editing, mathematical reasoning, model watermarking, and even jailbreak attacks. The tutorial also delves into advanced concepts like LLM steganography, multimodal models, GUI agents, and AI agent security. Additionally, it features a newly launched series on full-process LLM development in collaboration with Huawei Ascend, providing comprehensive guidance with PPTs, experimental manuals, and videos.

GNNs-for-NLP is a comprehensive resource offering code examples and tutorial materials for applying Graph Neural Networks (GNNs) to Natural Language Processing (NLP) tasks. Originating from presentations at EMNLP 2019 and CODS-COMAD 2020, this GitHub repository provides practical implementations using PyTorch 1.x and TensorFlow 1.x, compatible with Python 3.x. It features simplified GCN model implementations, extensions for relation extraction and word embeddings, and additional resources like theoretical write-ups and recent GNN papers. This tool is ideal for researchers and students looking to understand and implement graph-based deep learning methods in NLP.

machine-learning-deep-learning-notes provides a comprehensive learning path and knowledge summary for machine learning and deep learning, designed for modern AI development. It advocates a "practice first, then theory" approach, encouraging users to build projects and then delve into underlying principles as needed. The resource covers core concepts in mathematics, Python libraries like NumPy and Pandas, and practical applications of machine learning algorithms. It also delves into deep learning frameworks such as PyTorch and TensorFlow, with a strong focus on large language models (LLMs), multimodal AI, and AI agents. The guide includes structured learning paths for beginners and advanced users, emphasizing rapid skill acquisition and practical project building.

LLMBook-zh.github.io is an open-source project providing a comprehensive Chinese textbook titled "大语言模型" (Large Language Models). Authored by Zhao Xin, Li Junyi, Zhou Kun, Tang Tianyi, and Wen Jirong, this book aims to popularize and disseminate the latest advancements in large model technology. It offers a systematic framework and roadmap for LLM technologies, making it ideal for beginners with a background in deep learning. The project includes PDF courseware, code snippets on GitHub, and related teaching videos on Bilibili, serving as a valuable resource for both academic study and practical application. The content covers essential topics such as pre-training, fine-tuning, alignment, and prompt engineering, drawing from the authors' extensive research and practical experience in developing large models.

PyTorch-Tutorial-2nd is a comprehensive, open-source tutorial designed for individuals ranging from beginners to experienced deep learning engineers. It systematically covers PyTorch fundamentals, including environment setup, data handling, model building, optimization, and visualization. The tutorial delves into practical applications across computer vision (image classification, segmentation, object detection, GANs, Diffusion models), natural language processing (RNN, LSTM, Transformer, BERT, GPT models for text classification, machine translation), and large language models (Qwen, ChatGLM, Baichuan, Yi, GPT Academic). Furthermore, it provides in-depth guidance on industrial deployment, covering ONNX and TensorRT principles, model quantization (PTQ, QAT), and acceleration techniques, enabling users to master PyTorch for real-world project implementation.

Tutorial, developed by InternLM, is an open-source learning platform designed for developers to master large language models (LLMs) and vision-language models (VLMs). It offers a structured curriculum through 'camps' with challenges, documentation, and video tutorials covering foundational topics like Linux, Python, and Git, as well as advanced concepts such as prompt engineering, RAG implementation, model fine-tuning with XTuner, and deployment with LMDeploy. The platform also includes practical applications like building intelligent agents with Lagent and multi-modal model deployment. Participants can earn computational resources and certificates upon completing advanced challenges, fostering a community-driven learning environment.

Prompt-Engineering-Guide-zh-CN is an open-source, continuously updated guide to prompt engineering, specifically tailored for Chinese speakers. It serves as a comprehensive resource for understanding and optimizing prompts for large language models (LLMs). The guide covers various aspects of prompt engineering, including introductory concepts, basic and advanced prompting techniques, applications, and specific guidance for ChatGPT. It also compiles relevant papers, lectures, notebooks, and tools, making it an invaluable resource for researchers and developers looking to enhance their understanding and application of LLMs.

Voicefy is a text-to-speech platform that leverages neural voices to convert text into convincing human-like speech, primarily in Portuguese. It supports a wide range of applications, from producing audiobooks and podcasts without the need for recording studios to dubbing videos in 40 languages. The tool is also highly effective for e-learning, enabling automatic narration of courses, and for developing natural-sounding virtual assistants with low latency. Voicefy offers a free tier and boasts over 120 neural voices, making it a versatile solution for content creators, businesses, and developers seeking high-quality, automated voice narration.

Build-your-ai-coding-assistant is an open-source project and comprehensive guide detailing how to construct a custom AI-assisted coding tool, similar to GitHub Copilot or JetBrains AI Assistant. It provides a step-by-step approach for developers, covering the entire lifecycle from designing IDE plugins and selecting appropriate AI models to building high-quality datasets and fine-tuning models. The guide emphasizes practical application, using examples like DeepSeek Coder and Intellij IDEA, and discusses various AI coding scenarios such as code completion, explanation, generation, and review. It also delves into architectural considerations for balancing model speed and quality, and different contextual engineering approaches to enhance AI assistance.

Lhy_Machine_Learning serves as a comprehensive open-source repository for machine learning course materials and assignments from Professor Li Hongyi's spring courses spanning 2021, 2022, and 2023. Hosted on GitHub, this resource offers lecture slides, videos, and practical assignments covering fundamental concepts to advanced topics like Diffusion Models, Large Language Models, and Generative AI. It includes direct links to course videos, lecture notes, and homework assignments, often with accompanying code and platforms like Kaggle or JudgeBoi for submission. The repository is actively maintained with regular updates on topics and assignments, making it an invaluable resource for students and researchers looking to deepen their understanding of machine learning.

Artificial-Intelligence-and-Machine-Learning is a GitHub repository curated by emilmont, offering a comprehensive collection of algorithm implementations and homework solutions. This resource is specifically designed to complement Stanford's online courses, including 'Introduction to Artificial Intelligence,' 'Introduction to Machine Learning,' 'Artificial Intelligence for Robotics,' 'Computational Investing, Part I,' and 'Natural Language Processing.' It covers a wide array of topics such as search algorithms, Bayes networks, neural networks, support vector machines, K-means clustering, Monte-Carlo localization, A* search, PID controls, SLAM, and Hidden Markov models. The repository is open-source, making it freely accessible for students, AI enthusiasts, and developers to study and utilize the code.

Learn Prompting Pro is an AI detector tool designed to accurately identify content generated by AI models such as ChatGPT and Claude. Utilizing advanced multi-algorithm analysis, the tool boasts over 95% accuracy in detecting AI-generated text. It provides instant results without requiring any user signup, making it a convenient solution for quickly verifying content authenticity. The platform emphasizes its ability to perform sentence-level detection, offering detailed insights into the origin of text. This free utility application is ideal for users needing to distinguish between human-written and AI-generated content efficiently.

HuggingLLM is an open-source project dedicated to making Large Language Models (LLMs) accessible and understandable for a broad audience, including those without a deep NLP or algorithm background. The project aims to introduce the principles, usage, and applications of ChatGPT and other LLMs, enabling users to leverage these powerful AI tools to create their own AI products and services. It provides detailed learning guides, practical examples, and code implementations focusing on how to use LLM APIs for various tasks such as QA, text classification, and text generation. The project emphasizes adapting to the rapidly evolving AI landscape and encourages hands-on practice with both international and domestic LLM APIs like GLM and Qwen. It's designed for learners with some programming experience who want to apply LLMs to solve real-world problems.

notes is an open-source GitHub repository dedicated to providing educational resources and materials for learning about Machine Learning and Artificial Intelligence. It covers a wide array of topics including Deep Learning, Natural Language Processing, Reinforcement Learning, Bayesian Inference, Causal Inference, and Knowledge Representation and Reasoning. The repository is structured with individual markdown files for each topic, making it easy to navigate and study specific areas of AI. It is an excellent resource for anyone looking to deepen their understanding of AI concepts, from foundational theories to recent research papers.

AI-fundermentals is an extensive open-source learning resource dedicated to artificial intelligence infrastructure. It offers a complete technical stack, ranging from fundamental hardware concepts to advanced applications. The content delves into critical areas such as GPU architecture and programming, CUDA development, large language models (LLMs), AI system design, performance optimization, and enterprise-level deployment. This resource is designed to provide AI engineers, system architects, GPU programmers, LLM application developers, and technical researchers with a structured learning journey and practical guidance. It covers topics like AI cluster operations, cloud-native AI platforms, model training and fine-tuning, RAG and tools, agentic systems, and inference systems, making it a valuable asset for anyone looking to deepen their understanding of AI infrastructure.

AI-Notes is an extensive open-source repository offering a comprehensive series of notes and code examples focused on Artificial Intelligence and Deep Learning. The collection spans various critical areas, including Mathematics Fundamentals, Python Practices, and Natural Language Processing (NLP) Applications. It also provides practical guidance on tools and frameworks such as Scikit, TensorFlow, and PyTorch. This resource is designed for individuals looking to deepen their understanding and practical skills in data mining, machine learning, and deep learning, with content presented in both Markdown (.md) and Jupyter Notebook (.ipynb) formats for easy access and experimentation.

Hands-On-Large-Language-Models-CN is a comprehensive open-source educational project that serves as the Chinese translation of the popular "Hands-On Large Language Models" book by Jay Alammar and Maarten Grootendorst. This resource goes beyond a direct translation by providing more detailed code annotations and incorporating the author's own insights into various concepts. A key differentiator is its adaptation for the Chinese network environment, offering Notebook versions that are optimized for faster access without the need for VPNs, particularly useful for users within China. The project includes a structured curriculum covering topics from basic tokens and embeddings to advanced prompt engineering, RAG, and multimodal LLMs, with accompanying video explanations on platforms like Bilibili. It also provides practical guidance on setting up development environments and offers links to cloud GPU resources.

101.school is an innovative AI-powered platform designed to help users teach themselves anything by generating comprehensive, multi-week online courses. Users can either input a specific topic to have a custom course created by AI, or select from a wide array of pre-generated courses spanning various domains like sciences, engineering, arts, business, and personal development. The platform focuses on delivering structured learning experiences, with courses often broken down into weekly modules. This tool is ideal for individuals seeking to acquire new skills or knowledge efficiently, offering a flexible and personalized learning journey without the need for traditional educational institutions.

StudyWithGPT is an innovative AI-powered learning hub designed to provide personalized full-stack tech tutorials. Leveraging GPT technology, it creates customized lesson plans for users interested in a wide range of technologies, including Python, Java, JavaScript, PHP, Golang, front-end, back-end, DevOps, and microservices. The platform acts as a 24/7 AI full-stack mentor, breaking down complex knowledge points and offering patient, easy-to-understand explanations. Users can ask questions and receive immediate assistance, eliminating the need to search for scattered resources or rely on human experts. StudyWithGPT aims to streamline the learning process by tailoring content to individual learning objectives and providing continuous support.

Daptar is an AI-powered learning platform designed to deliver personalized educational experiences, specifically tailored for Indian curriculums. The platform leverages artificial intelligence to offer customized learning resources and automated tools, aiming to enhance the educational journey for students. While the live website currently redirects, the tool's core purpose is to provide an adaptive and individualized approach to learning, making education more accessible and effective through technology. Its focus on Indian curriculums suggests a specialized approach to content and pedagogical methods, catering to the specific needs of that educational system.

The Egg.ai is an online collaborative school dedicated to Artificial Intelligence, primarily for Spanish speakers. It aims to address the talent gap in AI by offering practical, task-based learning modules designed by international experts. The platform caters to individuals looking to learn AI from scratch, those with some programming knowledge seeking specialization, and professionals wanting to understand AI's impact on their careers. Users can learn at their own pace, choosing modules based on their interests and receiving certifications upon completion. The curriculum covers foundational computing, data exploitation, machine learning, and the business impact of AI, all while emphasizing ethical considerations.

Narration Box is an AI-powered text-to-speech and voice cloning platform designed to help users create professional voiceovers with ultra-realistic AI voices. It boasts a massive library of over 1500 lifelike AI voices and supports 140 languages and accents, making it highly versatile for global content creation. The platform includes an easy-to-use in-browser Studio for scripting, generating, and refining voiceovers, allowing control over timing, tone, and emotion. Users can clone any voice within seconds from a short audio sample, maintaining original tone and personality. Narration Box also features emotional performance capabilities and multi-speaker options, making it suitable for diverse applications like audiobooks, podcasts, and video content.

Didática Tech is an educational platform dedicated to making complex topics in Artificial Intelligence, Data Science, and Programming accessible and easy to understand. The platform offers a variety of courses covering essential subjects such as Python programming, Machine Learning with Python and R, Power BI, and Deep Learning. Users can access complete courses and exclusive materials designed to be didactic and straightforward. Didática Tech provides flexible and affordable plans, including combo deals and free introductory courses on topics like 'What is AI?' and 'How Machine Learning Works.' It aims to equip learners with practical skills for careers in technology.

Ragbook-notebooks is an open-source repository that compiles all the notebooks for the "Building LLMs for Production" book by Towards AI. It serves as a practical resource for developers and researchers interested in Retrieval-Augmented Generation (RAG) and large language models. The repository includes detailed examples and tutorials on various topics, such as understanding transformer architectures, mastering prompt engineering techniques, and building robust applications using frameworks like LangChain and LlamaIndex. It also covers advanced RAG concepts, agent creation, and fine-tuning LLMs, making it a comprehensive companion for those looking to implement LLMs in production environments.