Research & Education

deep-learning-from-scratch-2 is a comprehensive support site for the book "Deep Learning from Scratch 2 ―Natural Language Processing Edition" (O'Reilly Japan, 2018). This GitHub repository offers all the source code used throughout the book, making it an invaluable resource for readers looking to implement and experiment with the concepts discussed. The repository is meticulously organized by chapter, with dedicated folders for each, alongside common utilities and dataset-related code. It requires Python 3.x, NumPy, and Matplotlib, with optional support for SciPy and CuPy. The code is released under an MIT license, allowing for free commercial and non-commercial use. Additionally, the site provides links to errata and contact information for reporting new errors, ensuring the accuracy and usability of the learning materials.

Machine-learning-articles is a comprehensive GitHub repository featuring a collection of articles on various machine learning topics. These articles, originally penned by Christian Versloot for MachineCurve.com between May 2019 and February 2022, are now archived here for public access. The repository covers a wide range of subjects including deep learning, clustering, TensorFlow, PyTorch, Keras, and Scikit-learn. Users can find detailed explanations and practical examples on topics such as neural networks, GANs, LSTMs, activation functions, and various machine learning algorithms. It serves as a valuable resource for anyone looking to deepen their understanding of machine learning concepts and implementations.

llms-from-scratch-cn is an open-source educational project by Datawhale, designed to help developers and researchers build large language models (LLMs) from the ground up. It offers a comprehensive learning path, emphasizing practical implementation over theoretical concepts. The project focuses on understanding LLM architecture, providing step-by-step tutorials to construct models such as GLM4, Llama3, and RWKV6. It includes detailed code examples, covering encoding, pre-training, and fine-tuning processes, making it accessible for individuals with basic Python and PyTorch knowledge to delve deep into LLM principles.

LLM-quickstart is an open-source guide designed to help users quickly get started with large language models (LLMs). It offers comprehensive resources for both theoretical learning and practical fine-tuning of LLMs. The guide provides detailed instructions for setting up a development environment, including installing necessary software like CUDA Toolkit, Miniconda, and Jupyter Lab. It also outlines hardware requirements, specifically recommending a GPU with at least 16GB of VRAM, such as an NVIDIA Tesla T4. The project includes practical examples and configurations for working with various LLM components and frameworks, making it a valuable resource for those looking to dive into the world of large language models.

LLM-RL-Visualized offers a comprehensive collection of over 100 original architectural diagrams to systematically explain large language models (LLMs) and reinforcement learning (RL). This resource delves into the core principles of LLMs and Vision-Language Models (VLMs), various training algorithms such as RLHF, GRPO, DPO, SFT, and CoT distillation, as well as optimization techniques like RAG. Authored by the creator of "Large Model Algorithms," it serves as a valuable visual aid for understanding complex AI concepts. The repository is continuously updated with corrections and additions, providing high-definition diagrams and scalable SVG vector images for detailed study.

machine-learning-interview-questions is a valuable resource designed to help individuals prepare for Machine Learning interviews. This GitHub repository compiles a wide array of potential interview questions across key domains. Users can find dedicated sections for Deep Learning, General Machine Learning, and Mathematics for Machine Learning, ensuring a holistic preparation approach. The structured format allows for easy navigation through different topics, making it an efficient tool for job seekers aiming to strengthen their understanding and readiness for technical interviews in the AI and ML fields.

MLAPP_CN_CODE is an open-source GitHub project dedicated to providing a comprehensive Chinese translation of Kevin P. Murphy's influential textbook, "Machine Learning: A Probabilistic Perspective." Beyond just translation, the project also includes Python implementations of the algorithms discussed in the book, making complex concepts more accessible. Users can find code files directly linked to the graphics within the translated articles, facilitating a deeper understanding of the theoretical material through practical application. The project is actively maintained, with recent updates covering topics like deep learning, decision theory, optimization, and information theory, ensuring its relevance and timeliness for students and researchers alike.

DLInterview is a comprehensive GitHub repository designed to help individuals prepare for deep learning interviews. It compiles a wide range of questions and answers across critical domains such as mathematics, machine learning fundamentals, deep learning, computer vision, and basic algorithms. The repository also includes resources for algorithm languages and general technical interview knowledge, drawing from established computer science literature. It aims to provide a systematic and organized collection of study materials, making it an invaluable resource for students and job seekers in the AI and machine learning fields.

My Clever AI provides a comprehensive suite of AI tools designed to streamline various tasks, from web design to learning and photography. Users can leverage AI to generate complete web pages, individual web elements, or even redesign existing websites. The platform also offers advanced AI photo editing capabilities, including restoring old photos, generating AI headshots, and virtual try-on features. For learning, My Clever AI includes an AI teacher that provides personalized lessons and answers. Additionally, it offers tools for AI writing assistance, character creation, PDF interaction, and calorie tracking, making it a versatile solution for both creative projects and professional development. The platform is accessible via web, mobile apps, and a Chrome extension, ensuring a seamless experience across devices.

Starter-Guide, developed by the PKU-DAIR team, is an open-source repository designed to provide a comprehensive guide for beginners in the fields of data management (DM) and artificial intelligence (AI). It consolidates core papers and shared experiences from the team to help newcomers quickly familiarize themselves with cutting-edge areas and build a solid technical foundation. The guide covers various research directions including AI systems, AutoML, Database, AI Agent, Data-Centric ML, Diffusion Models, AI for Science, and Graph. It aims to support users in their learning and research journeys, whether they are just starting out or looking to deepen their understanding.

vibe-vibe is an open-source, systematic tutorial designed to make AI-assisted coding accessible to everyone, regardless of prior programming experience. It introduces the concept of "Vibe Coding," where users interact with AI through natural language to create applications, shifting the focus from writing code to conversational creation. The tutorial is structured into four main sections: a foundational 'Basic' part for AI programming essentials, an 'Advanced' section covering full product delivery, a 'Practice' section with project-based learning, and a 'Quality Articles' section for continuous learning. It aims to empower individuals, from students to entrepreneurs, to quickly realize their ideas and enhance productivity using AI.

TF-Tutorials is an open-source repository offering a comprehensive collection of deep learning tutorials implemented in TensorFlow and Python. It features Jupyter notebooks covering diverse topics such as Deep Convolutional Generative Adversarial Networks (DCGAN), InfoGAN, deep layer visualization, and comparisons of deep network architectures like ResNet, HighwayNet, and DenseNet. The repository also includes tutorials on implementing basic Recurrent Neural Networks (RNN-TF) and using t-SNE for visualizing intermediate layer representations. This resource is ideal for individuals looking to understand and apply TensorFlow in practical deep learning projects, providing clear, executable examples.

Prompt-Engineering-Guide-Chinese is a comprehensive, open-source guide designed to help individuals understand and master prompt engineering. It is a translated and updated version of a popular English guide, specifically enhanced with AIGC (AI-Generated Content) prompt sections to make the learning process more accessible for Chinese-speaking users. The guide covers the development and optimization of prompts for effectively utilizing large language models (LLMs) across various applications and research topics. It aims to improve understanding of LLMs' capabilities and limitations, offering insights for researchers to enhance LLMs' performance on tasks like Q&A and arithmetic reasoning, and for developers to design powerful prompting techniques for LLM interfaces.

Machine_Learning_Study_Path is an open-source GitHub repository designed to guide individuals through their machine learning journey. It offers a structured collection of resources, including popular online courses from platforms like Coursera and NetEase Cloud Classroom, recommended books covering topics from Keras to deep learning, and essential frameworks such as TensorFlow and PyTorch. The repository also features a curated list of influential machine learning blogs from institutions like Google Brain and DeepMind, making it an invaluable resource for students and professionals looking to deepen their understanding and practical skills in machine learning.

WTF Does This Company Do? leverages GPT-3 to demystify corporate jargon and explain what companies actually do. Users can input a company name or website URL, and the tool generates a plain-language explanation. Beyond just explanations, it also offers a 'roast the website' feature for humorous, albeit potentially offensive, critiques. The tool is designed to help users quickly grasp the core business of various companies, making it useful for anyone encountering unfamiliar business descriptions or seeking a quick, AI-powered summary. It is built by @krishnerkar and supported by donations.

Mockaroni is an AI-powered platform specifically developed to help job seekers practice and improve their interview skills. The tool simulates real-world interview scenarios, allowing users to gain experience and confidence before actual job interviews. It offers personalized feedback on performance, helping users identify areas for improvement in their responses, body language, and overall presentation. Mockaroni aims to enhance a user's ability to articulate their skills and experiences effectively, making it a valuable resource for anyone looking to boost their chances of success in the competitive job market.

wonderful-prompts is an open-source project featuring a meticulously curated collection of high-quality Chinese prompts designed to significantly enhance the usability and effectiveness of ChatGPT. This resource provides hundreds of prompts, complete with illustrative examples and usage guidelines, making it easier for users to master AI interactions. The project is continuously updated and encourages community contributions. It also offers a comprehensive ChatGPT Chinese guide, covering tutorials, selected open-source projects, and other AI tools, making it an invaluable resource for anyone looking to deepen their understanding and application of ChatGPT.

Build-A-Large-Language-Model-CN is an open-source project on GitHub that offers a Chinese translation of the e-book "Build a Large Language Model (From Scratch)". This resource is designed for learners eager to delve into the core principles and practical implementation of large language models (LLMs), including architectures like GPT, their training processes, and application development. The project aims to make this valuable educational material accessible to a wider Chinese-speaking audience. It includes the translated Chinese version, the original English e-book, and all translated images. The author also provides personal insights and interpretations to enhance understanding, alongside practical code examples for hands-on learning.

Islamic Dream offers AI-powered interpretation of dreams, grounded in centuries of Islamic scholarship. The tool analyzes dream symbols and context using the works of classical scholars such as Ibn Sirin, Al-Nabulsi, and Ibn Qayyim al-Jawziyyah. Users describe their dreams, and the AI provides a detailed, personalized interpretation that references relevant Quranic verses and Hadith. This platform aims to make traditional Islamic dream knowledge accessible, serving as an educational tool for understanding the spiritual communication inherent in dreams. It highlights the three types of dreams in Islam—Ru'ya, Hulum, and Hadith al-Nafs—and provides guidance on how to respond to bad dreams according to prophetic advice.

MTBook, authored by Tong Xiao and Jingbo Zhu from NEUNLPLab / NiuTrans Research, is a comprehensive textbook and tutorial on machine translation. It systematically introduces the foundational knowledge and modeling methods of machine translation, while also discussing advanced frontier technologies. The content is structured into four parts, covering machine translation basics, statistical machine translation, neural machine translation, and advanced topics. It is designed for senior undergraduate and graduate students in computer science and AI, as well as researchers in natural language processing, particularly those focused on machine translation. The book's source code is open-source, and a full PDF version is available, making it a valuable resource for in-depth study and reference.

بوابة الذكاء الاصطناعي - Bawaba AI serves as a comprehensive platform for individuals interested in artificial intelligence, offering a wealth of information in Arabic. The site features up-to-date news on global and Arab AI advancements, in-depth articles, and practical tips. It also covers specialized equipment, applications, and profiles of key figures and organizations in the AI field. The platform aims to help users develop their skills and stay informed about the rapidly evolving AI landscape, with content ranging from smart cars and smartphones to robotics and cryptocurrency news, all presented with a focus on AI's role and impact.

book_DeepLearning_in_PyTorch_Source is an open-source GitHub repository containing the source code for a book titled "Deep Learning Principles and PyTorch Practice." This resource is designed to help users understand deep learning concepts and their practical implementation using the PyTorch framework. It covers a wide range of topics, from introductory PyTorch concepts to advanced applications like generative models, transfer learning, and reinforcement learning. The repository includes code examples for tasks such as text classification, image style transfer, and neural machine translation, making it a valuable learning tool for students and developers looking to gain hands-on experience with deep learning in PyTorch.

CramJam is an innovative AI-powered learning tool designed to revolutionize the study process. It allows users to effortlessly convert existing PDF slides, notes, or quizzes into a variety of customizable study tools. With just a few clicks, students and educators can generate instant quizzes, complete with citations, to enhance their learning and teaching experiences. The platform aims to simplify the creation of personalized study materials, making it easier to prepare for exams or reinforce understanding of complex topics. CramJam focuses on efficiency and customization, providing a streamlined approach to academic preparation.

Awesome-DeepLearning-500FAQ is a comprehensive open-source resource designed to help individuals understand deep learning concepts through a question-and-answer format. It covers a wide range of topics, including foundational knowledge in probability, linear algebra, machine learning, and deep learning, as well as specialized areas like computer vision, generative adversarial networks, and reinforcement learning. The content is structured into 18 chapters, totaling over 500,000 words, making it a substantial learning aid. Users can access the material in both HTML and PDF formats, with the HTML version offering direct navigation via anchored links for quick access to specific chapters. This resource is ideal for self-study and for those seeking to deepen their understanding of complex AI and machine learning subjects.