Research & Education

Openmed Clinical NER is an AI-powered tool designed for named entity recognition (NER) within clinical text. Users can provide medical text and select a specialized model, such as Oncology Detection, to extract specific medical terms. The tool is capable of identifying diseases, drugs, and genes, and allows users to adjust a confidence threshold for the extraction process. This makes it particularly useful for medical research, clinical data analysis, and any application requiring precise identification of medical entities within textual data. Its specialization in cancer, genetics, and oncology entities provides a focused and powerful solution for these domains.

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.

makeyourownneuralnetwork is an open-source code repository hosted on GitHub, designed to accompany the 'Make Your Own Neural Network' book. It offers practical examples and implementations of neural network concepts, making it an invaluable resource for individuals looking to learn and understand the fundamentals of neural networks through hands-on coding. The repository includes various Jupyter Notebooks covering topics such as MNIST dataset handling, neural network implementation, loading custom images, and backquerying. This resource is ideal for students and self-learners who want to dive deep into the mechanics of neural networks and build their own models from scratch.

nn-from-scratch is an open-source project available on GitHub that provides a practical implementation of a neural network from scratch. This resource is designed for individuals looking to deepen their understanding of how neural networks function at a foundational level. The project includes Python code, an iPython notebook for interactive learning, and a related blog post that explains the concepts in detail. It covers the setup of a virtual environment and installation of necessary requirements, making it accessible for hands-on learning and experimentation with neural network architectures.

qikqiak.com is a comprehensive blog dedicated to exploring various cutting-edge technologies. It offers in-depth articles and resources on topics such as ChatGPT, containerization, Kubernetes, DevOps practices, Python, and Golang. The platform also delves into microservices architecture and other related technical subjects, providing valuable insights for developers and technology enthusiasts. The blog aims to keep its audience informed about the latest trends and best practices in the tech world, making complex concepts accessible through detailed explanations and practical examples. It serves as a knowledge hub for those looking to deepen their understanding and skills in these rapidly evolving domains.

AnyQuestions.ai is an AI-powered education platform designed to enhance the learning experience for students and educators. Users can upload various documents, and the platform leverages AI to generate comprehensive answers, complete with citations for accuracy and reliability. Beyond just answering questions, AnyQuestions.ai also creates AI-generated flashcards, interactive learning maps, and custom quizzes. These features are specifically designed to optimize study habits, reinforce understanding, and provide personalized learning paths, making it a versatile tool for both self-study and educational content creation.

BRAID UK is a 3-year national research programme funded by the UKRI Arts and Humanities Research Council (AHRC), led by the University of Edinburgh in partnership with the Ada Lovelace Institute. It focuses on integrating Arts, Humanities, and Social Science research more fully into the Responsible AI ecosystem. The program aims to bridge the divides between academic, industry, policy, and regulatory work on responsible AI, with over £18 million in funding from 2022 to 2028. BRAID UK offers various projects including demonstrator projects, fellowships, scoping projects, and artist commissions, alongside opportunities for flexible impact funding and a Responsible AI Innovation course for SMEs.

10,000 Original AI Godfathers is presented as a digital time capsule, highlighting individuals who played significant roles in the foundational stages of artificial intelligence. This initiative aims to document and recognize researchers, scientists, engineers, investors, and dedicated AI users, collectively referred to as 'AI Godfathers.' The project seeks to preserve the legacy of these early contributors, offering insights into the diverse talents and efforts that shaped the nascent AI landscape. While the website content currently displays a parked domain message, the original concept suggests a focus on historical documentation and recognition within the AI community.

Autopilot-Notes is a comprehensive open-source knowledge base designed for systematic learning and mastery of autonomous driving technology. It covers a wide array of topics including foundational theories, hardware components, perception algorithms, localization techniques, planning strategies, and control systems. The repository also features in-depth analyses of solutions from leading manufacturers like Tesla, Baidu Apollo, and Huawei ADS. With daily updates on industry news and technical advancements, Autopilot-Notes serves as an invaluable resource for students and developers looking to stay current with the rapidly evolving field of autonomous vehicles. It emphasizes practical application with content on simulation, deployment, and optimization.

Chinese-number-gestures-recognition is an open-source Android application designed to recognize Chinese number gestures from 0 to 10 using a convolutional neural network (CNN). The project includes both the Android app code for real-time gesture recognition via a mobile camera and PC-side code for data processing and model training. It supports development environments like Python 3.6 with TensorFlow-gpu and Android Studio with TensorFlow Lite and OpenCV. The project also provides datasets, including raw images, data-augmented images, and compressed H5 datasets, along with pre-trained models. While the PC-trained models show high accuracy, the app's real-world performance can vary in complex environments.

BabelDuck is a beginner-friendly, highly customizable AI conversation practice application designed for language learners of all levels. It focuses on minimizing barriers and cognitive load for oral expression practice by offering features like multiple conversation management, custom system prompts, and streaming responses. Users can seek grammar, translation, or expression refinement suggestions from the AI without interrupting the current conversation, and even start sub-conversations for further discussion. The application supports voice input and response, integrates multiple LLM AI services with seamless switching, and stores data locally to ensure user privacy. It also provides individual preference settings for different conversations, a multilingual interface, and built-in tutorials.

Ellen AI is a smart AI companion with voice capabilities, offering users the opportunity to own and customize their own AI. The product includes full source code for the project and a chatbot template, along with a step-by-step video guide on creation and customization. Users can rebrand, resell, and improve the source code, making it a versatile tool for developers and entrepreneurs. It allows for the application of the same concept to build various AI products such as AI girlfriends, chatbot assistants, and extensions with voice. The product is available for a one-time purchase, granting perpetual ownership and access to the code immediately, with the video guide added shortly after purchase.

Nerd AI - Math Problem Solver is an AI-powered mobile application designed to be a comprehensive study companion for students. The app allows users to scan math problems and receive instant, step-by-step solutions, simplifying complex mathematical challenges. Beyond math, Nerd AI also offers assistance with various academic tasks, including writing support, language learning tools, and content summarization capabilities. This multi-functional approach aims to make learning more accessible and efficient, providing a versatile AI interface for students seeking help across different subjects. The tool is developed by Codeway, a company focused on AI-powered mobile app development.

deep-learning-from-scratch-4 is an open-source GitHub repository that serves as the support site for the book "Deep Learning from Scratch 4: Reinforcement Learning Edition" (O'Reilly Japan, 2022). It provides all the source code used in the book, organized by chapter, along with common utility code. The repository also offers Jupyter Notebook versions of the code, which can be run directly on cloud services like Google Colab, Kaggle Notebook, and Studio Lab for interactive learning. It supports Python 3.x and requires libraries such as NumPy, Matplotlib, OpenAI Gym, and DeZero (or PyTorch). The project is licensed under the MIT License, allowing for free commercial and non-commercial use, making it an excellent resource for students and developers exploring reinforcement learning.

MathGPT is an all-in-one AI math solver and homework helper designed to assist students with algebra, geometry, calculus, statistics, physics, accounting, and chemistry problems. Trusted by over 2 million students globally, it offers instant step-by-step solutions and unique AI-powered video explanations with engaging animations and diagrams. Users can upload homework via text, photos, or PDFs and receive personalized tutoring, including the ability to ask follow-up questions and create custom interactive quizzes. MathGPT also features graphing software and is available as a mobile app on iOS and Android, making it a comprehensive tool for understanding complex STEM concepts.

Giotto-tda is a high-performance topological machine learning toolbox implemented in Python, designed to facilitate advanced data analysis and machine learning research. Built on top of the scikit-learn ecosystem, it offers robust algorithms for topological data analysis (TDA). The toolbox is part of the Giotto family of open-source projects and is distributed under the GNU AGPLv3 license. It supports Python 3.7+ and integrates with popular libraries like NumPy, SciPy, and Plotly. Giotto-tda is the result of a collaborative effort between L2F SA, EPFL, and HEIG-VD, making it a reliable tool for researchers and data scientists working with complex datasets.

The LangChain-Chinese-Getting-Started-Guide is an open-source tutorial designed to help Chinese speakers learn and utilize the powerful LangChain framework. It covers essential concepts such as LLM invocation, prompt management, document loaders, text splitters, vector stores, chains, and agents. The guide provides practical examples, including performing Q&A with OpenAI models, integrating with Serpapi for internet searches, and summarizing long texts. It also addresses common challenges like API token limits and offers solutions using LangChain's features. The tutorial is actively maintained on GitHub, with updates and code examples available for hands-on learning.

KB2E is a knowledge graph embedding tool developed as a subproject of THU-OpenSK. It provides implementations for several prominent knowledge graph embedding algorithms, including TransE, TransH, TransR, and PTransE. These algorithms are crucial for representing entities and relations in a knowledge graph as low-dimensional vectors, enabling various downstream tasks like link prediction and entity classification. While the project offers valuable resources for researchers and developers interested in knowledge graph embeddings, it is important to note that KB2E is no longer actively maintained. Users are advised to transition to the newer and actively supported OpenKE package for continued development and support in this domain.

Machine-learning-engineering-for-production-public serves as the official public repository for DeepLearning.AI's Machine Learning Engineering for Production Specialization. This resource is designed to support students and professionals in understanding the intricacies of deploying machine learning models into real-world production environments. The repository contains various materials, including course content, labs, and other public resources relevant to the specialization's curriculum. While it provides valuable learning assets, the repository is currently not accepting pull requests for contributions. It is an essential companion for anyone undertaking the DeepLearning.AI MLEP Specialization, offering practical insights and foundational knowledge for machine learning engineering.

Machine-Learning-Interviews is an open-source GitHub repository designed to guide individuals through the preparation process for Machine Learning and AI technical interviews. This resource is particularly valuable for those targeting roles like Machine Learning Engineer and Applied Scientist at prominent tech companies. It covers essential interview modules such as general coding (algorithms and data structures), ML-specific coding, ML fundamentals, ML system design, and behavioral questions. The repository also includes a new section on Agentic AI Systems, reflecting the latest trends in AI engineering. Compiled from the author's personal experience and successful interview preparations, it offers a structured approach to mastering the diverse components of technical ML interviews.

machine-learning-yearning-cn is the Chinese translation of Andrew Ng's influential book, "Machine Learning Yearning." This resource is specifically designed to equip AI engineers and practitioners with practical, technical strategies for navigating the complexities of machine learning projects. It covers essential topics related to training and managing machine learning systems, offering insights into debugging, error analysis, and overall project optimization. The project is open-source and encourages community contributions to improve translation quality, making it a collaborative effort to disseminate crucial AI knowledge within the Chinese-speaking community. It serves as a valuable study assistant for those looking to deepen their understanding and application of machine learning principles.

named_entity_recognition is an open-source project dedicated to Chinese named entity recognition (NER), offering practical implementations of several prominent models. It includes Hidden Markov Model (HMM), Conditional Random Field (CRF), Bi-directional Long Short-Term Memory (BiLSTM), and a hybrid BiLSTM+CRF model. The project utilizes a resume dataset for training and evaluation, providing detailed accuracy, recall, and F1 scores for each model. It serves as a valuable resource for researchers and developers interested in NLP, particularly in the context of Chinese NER, allowing for direct comparison and understanding of different algorithmic approaches.

pml2-book, or "Probabilistic Machine Learning: Advanced Topics," is a comprehensive book authored by Kevin Murphy, focusing on advanced concepts within probabilistic machine learning. This resource is openly available as a PDF, primarily distributed through its GitHub repository, allowing for easy access and tracking of downloads and issues. It is designed for individuals who already possess a foundational understanding of machine learning and are looking to delve into more complex and specialized areas. The repository also includes various supplementary materials such as prefaces and tables of contents, offering a detailed overview of the book's structure and content.

MiVOLO-Demo is an AI-powered tool available on Hugging Face Spaces that allows users to upload an image and receive an estimation of the age and gender of individuals within it. The platform provides options to adjust detection settings, which can help improve the accuracy of the results. Developed by Irina Tolstykh, this web application falls under the AIApplication category and is licensed under Apache 2.0. It offers a straightforward interface for exploring AI capabilities in facial analysis, making it accessible for quick demonstrations and personal use.