Research & Education

Notebooks On The Hub is an AI application hosted on Hugging Face, designed to provide users with a platform for accessing and exploring AI notebooks. It enables users to create and customize static web pages by directly editing HTML files within the platform. This functionality is accessible through the Files and versions tab, allowing for immediate viewing of changes on the web page. The tool is part of the Hugging Face Spaces ecosystem, indicating its focus on community and collaborative development within the AI domain. It is particularly useful for individuals looking to experiment with or share AI-related code and demonstrations in an easily accessible web environment.

knowledge-graph-from-GPT is an open-source program designed to create an external memory module for language models, enhancing their ability to organize, access, and generate information. It functions as a wrapper for a language model in Python, allowing for the categorization and structuring of information, identification of knowledge gaps, and generation of questions. The tool addresses key language model shortcomings such as memory, logic, and interpretability by creating a human-interpretable knowledge graph. It supports various long-term applications including database generation, question answering, summarizing research, identifying conflicting information, and serving as an educational tool or flashcard assistant. The program also aims to facilitate hypothesis generation for scientific research by processing vast amounts of information and proposing novel ideas.

PINNpapers is a comprehensive, open-source repository maintained by the IDRL lab, dedicated to curating essential research papers on Physics-Informed Neural Networks (PINNs). Since PINNs have gained significant traction in scientific computing, this resource serves as a valuable collection of representative works in the field. The repository categorizes papers across various aspects of PINNs, including foundational models, parallel computing approaches, acceleration techniques, model transfer and meta-learning, probabilistic PINNs, uncertainty quantification, and diverse applications. It also lists relevant software libraries like DeepXDE and SciANN, providing links to papers and code where available. Researchers and practitioners can use this resource to stay updated on the latest advancements and foundational concepts in PINN research.

SAMMY Labs offers a deterministic and interpretable AI solution for legal and compliance needs. It transforms regulatory statutes and internal operating procedures into powerful legal engines. These engines are designed to audit accounts, generate regulator-ready reports, and ensure continuous system compliance. Key features include the ability to train SAMMY with internal knowledge, create personalized SAMMY Guides for customer support in various platforms like Slack and email, and robust analytics to understand user responses and improve product offerings. The tool also integrates directly with Slack for quick guide creation and sharing. SAMMY Labs aims to provide a living brain that achieves human-level understanding of a company's products and processes.

StudyX is an all-in-one AI study platform designed to enhance learning for students, educators, and professionals. It offers a comprehensive suite of AI-powered tools including homework help with step-by-step solutions, AI note-taking from various materials like PDFs and videos, and AI flashcard and quiz generators for effective exam preparation. Additionally, StudyX provides AI writing tools such as an AI detector, humanizer, plagiarism checker, and paraphraser to ensure originality and improve writing quality. The platform supports over 50 subjects and caters to different learning stages, from middle school to professional certification prep, making it a versatile resource for academic and professional development.

Awesome is an open-source GitHub repository offering a comprehensive collection of resources across various technical domains. It serves as a valuable knowledge base for individuals interested in bioinformatics, data science, and machine learning. The repository also includes extensive resources for popular programming languages such as Python, Golang, R, and Perl, along with sections for C, JavaScript, Linux, and Git. Users can find links to tools, tutorials, and libraries, making it a central hub for learning and development in these fields. Its curated nature ensures that the included resources are relevant and useful for both beginners and experienced practitioners.

ciml is an open-source repository offering comprehensive materials for "A Course in Machine Learning." It serves as a valuable resource for both students and educators, providing the full source code for the accompanying book. Beyond the core text, the repository includes a wealth of supplementary course materials such as detailed slides, informative documents, and practical laboratory exercises. This makes ciml an excellent tool for those looking to learn about machine learning through a structured curriculum or for instructors seeking ready-to-use content for their courses. The materials are designed to support a thorough understanding of machine learning concepts.

cs229-2018-autumn is a comprehensive repository offering all notes and materials from Stanford University's CS229: Machine Learning course, specifically from the Autumn 2018 edition. This resource includes detailed lecture notes, presentation slides, and various assignments, providing a complete academic package for students and enthusiasts. Additionally, it links to the corresponding lecture videos available on YouTube, enhancing the learning experience. The repository also contains problem sets, solutions, and project materials, making it an invaluable tool for self-study or supplementary learning in machine learning.

generative-ai-roadmap offers a comprehensive overview of generative AI, detailing its use cases and applications through a structured roadmap. This resource, available on GitHub, includes both original Chinese content and English translations of its diagrams and text. It covers the evolution of controllability in generative AI, its application directions, key application areas with typical examples, and the evolution of multimodal AI application capabilities. The project is licensed under a Creative Commons Attribution 4.0 International License, making it a valuable educational resource for anyone interested in understanding the landscape of generative AI.

efficient-dl-systems is an open-source GitHub repository offering comprehensive educational materials for the Efficient Deep Learning Systems course, taught at HSE University and Yandex School of Data Analysis. The repository includes a detailed syllabus, lecture notes, and seminar materials covering a wide range of topics, from foundational GPU architecture and CUDA API to advanced concepts like distributed training, large model optimization, and inference algorithms. It provides practical insights into performance measurement, mixed-precision training, data-parallel techniques, and deployment of deep learning models. The course content is structured week-by-week, making it an invaluable resource for students and researchers looking to deepen their understanding of efficient deep learning practices.

feature-engineering-book is the official GitHub code repository accompanying the book "Feature Engineering for Machine Learning" by Alice Zheng and Amanda Casari, published by O'Reilly in 2018. This resource is invaluable for students, researchers, and practitioners looking to implement the feature engineering techniques discussed in the book. The repository contains various Jupyter Notebooks covering topics such as binning, count features, log and Box-Cox transformations, interaction features, text processing (TF-IDF, chunking), regression on categorical variables, feature hashing, PCA, K-means clustering for featurization, and HOG image features. It also includes end-to-end recommender system examples, providing practical code for a deeper understanding of machine learning concepts.

ml_cheatsheet is an open-source resource offering a highly condensed, 5-page Machine Learning cheatsheet. This document is designed to provide a quick and accessible reference for the most popular machine learning algorithms and their core mechanics. It's an invaluable tool for students and professionals alike who need to review, understand, or quickly recall fundamental ML concepts and techniques. The cheatsheet is available as a PDF, making it easy to download and use for study or quick lookups. Its concise nature ensures that users can grasp key information without sifting through extensive documentation, making it particularly useful for exam preparation or rapid concept reinforcement.

Machine-Learning-A-Probabilistic-Perspective-Solutions is a GitHub repository offering comprehensive solutions to exercises found in Kevin Murphy's renowned 'Machine Learning: A Probabilistic Perspective' textbook. This resource is designed to aid students and researchers in understanding complex machine learning concepts by providing detailed, step-by-step solutions. The repository focuses on computational exercises, which are implemented in Python using Jupyter notebooks, making them interactive and easy to follow. Each solution includes an introduction, insight into the problem, the solution itself, and remarks, enhancing the learning experience. It serves as an invaluable educational tool for anyone studying machine learning.

Machine-Learning-homework is an open-source GitHub repository offering Matlab coding assignments specifically designed for the Machine Learning course by Andrew Ng on Coursera. This resource is invaluable for students looking to practice and reinforce their understanding of machine learning concepts through practical coding exercises. The repository also thoughtfully includes links to external solutions and resources, primarily in Chinese, providing additional support for learners. It serves as a practical companion for those undertaking the Coursera course, enabling them to work through the assignments and check their understanding.

sig-mlops is a Special Interest Group (SIG) within the Continuous Delivery Foundation (CDF) dedicated to Machine Learning Operations (MLOps). This open-source initiative aims to foster collaboration and drive standardization within the MLOps community. The group focuses on sharing best practices, developing documentation, and providing resources for professionals involved in the deployment, monitoring, and management of machine learning models. It serves as a hub for discussions, knowledge exchange, and contributions to the evolving field of MLOps, helping to streamline processes and improve efficiency in AI/ML development workflows.

resources is an open-source repository dedicated to curating and organizing Go-based data science resources. It serves as a central hub for developers and data scientists working with the Go programming language, offering a comprehensive collection of links to various community resources such as events, conferences, and blogs. Additionally, it provides an extensive list of tooling resources, including essential packages, libraries, and development tools specifically designed for data analysis, visualization, and machine learning tasks within the Go ecosystem. This makes it an invaluable asset for anyone looking to explore or deepen their work in data science using Go.

Self-Driving-Cars is an open-source repository hosted on GitHub, offering a comprehensive collection of Coursera open courses from the University of Toronto. This resource is specifically designed for individuals interested in the field of self-driving car technology, providing access to videos, subtitles, and PDF materials. It's particularly beneficial for postgraduate students and researchers aiming to work on automotive motion planning, offering a structured and in-depth learning experience. The repository includes courses covering topics from an introduction to self-driving cars to state estimation, visual perception, and motion planning. Users can download and watch the content, and a rough notebook based on subtitles is provided for better review.

stat479-machine-learning-fs19 offers comprehensive course material for the STAT 479: Machine Learning class taught by Sebastian Raschka at the University of Wisconsin-Madison. This GitHub repository serves as a central resource for students, covering a wide array of machine learning concepts from introductory topics like K-Nearest Neighbors to advanced subjects such as ensemble methods, model evaluation, and dimensionality reduction techniques. The material is organized into lectures, including practical computational foundations using Python, Anaconda, Jupyter Notebooks, NumPy, SciPy, and Scikit-Learn. It's an invaluable resource for students and educators looking for structured machine learning curriculum.

Theo-Docs is an open-source GitHub repository offering comprehensive guides for unlocking and utilizing various streaming services and AI tools. It provides detailed documentation for popular platforms such as Netflix, Disney+, Spotify, YouTube Premium, ChatGPT, and Gemini. Beyond streaming and AI, the repository also delves into practical topics like daily records, ESXI virtualization, OpenWrt router firmware, VPS guides, and information on various cloud service providers. This resource is ideal for users looking to optimize their digital experience across entertainment, AI applications, and personal server management.

Narrated Guide provides immersive, self-guided audio tours designed to enhance your travel experience. Users can explore destinations like London, Rome, or Kyoto with a personal storyteller, bringing local sights, sounds, and histories to life. The platform breaks down stories into segments, allowing travelers to read or listen to content that interests them most. It offers carefully crafted themed itineraries with optimized routes or the flexibility to create custom itineraries from scratch. Narrated Guide aims to provide an enriching travel experience at your own pace, without rigid schedules or awkward group tours, while also promoting sustainable tourism.

awesome-NeRF-papers is an Open Source repository that serves as a comprehensive collection of research papers related to Neural Radiance Fields (NeRF). It meticulously gathers publications from top-tier computer vision and machine learning conferences, including CVPR, ICCV, ECCV, NIPS, ICML, and ICLR. This resource is invaluable for researchers, academics, and students who need to track the rapid developments in NeRF technology. The repository is organized by conference and year, making it easy to navigate and find specific papers. It also includes summaries and counts of papers from various conferences, offering a quick overview of research trends and the volume of work being published in this field.

awesome-programming-books is a meticulously curated list of programming books, offering a wide array of topics essential for both aspiring and experienced developers. This resource encompasses fundamental areas such as Algorithms and Data Structures, Artificial Intelligence, Software Architecture, and Human–Computer Interaction. It also delves into specialized fields like Operating Systems, Database Systems, IT Security, Concurrency, Interpreters and Compilers, High-Performance Computing, Distributed Systems, Game Development, and Mathematical Optimization. Each category provides a selection of highly-regarded books, complete with ISBNs, making it an invaluable guide for students, educators, and professionals looking to deepen their knowledge or explore new domains within computer science and software engineering.

arXivTimes is a comprehensive repository designed for researching and sharing machine learning articles. It offers a structured approach to managing and disseminating information, including one-sentence summaries of papers and a system for tracking them via GitHub Issues. The platform also curates valuable datasets applicable to machine learning and lists tools that aid in model implementation. Furthermore, arXivTimes compiles conference-related papers, categorized by year and major conferences like NIPS, ICLR, and ICML, alongside information on conference deadlines and best paper awards. It encourages community contributions, allowing users to submit paper summaries following a clear template, fostering a collaborative environment for machine learning enthusiasts and researchers.

awesome-6d-object is a valuable open-source repository dedicated to collecting and organizing significant works in the field of 6 DoF (Degrees of Freedom) object pose estimation. This resource is particularly useful for researchers and developers in computer vision and deep learning, offering a curated list of papers, projects, and other materials. It covers various aspects of object pose estimation, including methods for 3D object reconstruction from a single view and techniques for 3D hand-object pose estimation. The repository aims to provide a centralized hub for staying updated on advancements and finding relevant information in this specialized domain.