Research & Education

DeepReg is a freely available, community-supported open-source toolkit designed for research and education in medical image registration using deep learning. It is built on TensorFlow 2 for efficient training and rapid deployment of models. The toolkit implements major unsupervised and weakly-supervised algorithms, along with their combinations and variants, focusing on growing and diverse clinical applications. All DeepReg Demos utilize openly accessible data, and it offers simple built-in command-line tools that require minimal programming. DeepReg operates under the Apache 2.0 license, promoting an open, permissible, and research-and-education-driven environment.

BioSketch is a free, user-friendly drag-and-drop tool specifically designed for scientists and researchers to create high-quality scientific illustrations. It simplifies the process of building publication-ready figures by providing an extensive library of biomedical icons. Users can easily assemble complex diagrams and visuals without needing advanced graphic design skills. The platform aims to empower the scientific community to produce stunning and accurate visual representations of their research, enhancing communication and impact in academic publications and presentations.

NearGuru, a product of Vidhyalink Technologies, is a hyperlocal education platform designed to simplify how students discover quality learning guidance. It connects students with verified tutors and coaching centers in their vicinity, catering to various needs, goals, and budgets. The platform offers a wide range of courses, including boards, competitive exams, co-curricular activities, skill-based learning, and college courses. Key features include tracking learning journeys, monitoring performance, managing expenses, and access to 100% verified centers and certified tutors. NearGuru aims to empower students with quality education through expert-led learning, personalized guidance, and flexible scheduling options.

GVHMR is an AI tool hosted on Hugging Face Spaces that specializes in 3D human pose estimation and visualization. Users provide input images, and the application processes them to output detailed 3D pose information. The tool sets up its necessary environment by downloading models and dependencies to perform its core function. While the live website indicates a runtime error, the intended functionality is to provide advanced human pose analysis, making it valuable for researchers, developers, and anyone interested in computer vision applications related to human movement and form.

Aibility provides a comprehensive AI assessment platform designed to evaluate and improve AI literacy and application within organizations. It offers individual AI profiles, team overviews, and organizational insights, identifying strengths, weaknesses, and untapped potential in AI usage. The platform measures AI proficiency through metrics like AI Stratég, SP Score, and iteration habits, providing detailed reports and actionable recommendations. Aibility also offers various programs, including a Superpowered Assessment, Superpowered Foundations workshops, and Vibe Coding Summer, to further develop AI skills. It helps companies track AI adoption, measure ROI of L&D initiatives, and identify internal AI leaders, ensuring compliance with regulations like the EU AI Act.

UADAMAGE is an AI and GIS company specializing in geospatial analytics for automatic damage monitoring. The platform leverages satellite and drone imagery alongside advanced computer vision to assess damage following war or natural disasters. It transforms these diverse data inputs into actionable insights, aiding governments, organizations, and partners in making data-driven decisions. UADAMAGE's core focus areas include infrastructure recovery, demining efforts, and environmental monitoring, providing critical information for post-disaster assessment and planning.

Business Machine Learning (BML) and Business Data Science (BDS) Applications is a comprehensive, open-source resource available on GitHub, offering a curated list of practical applications across diverse business functions. This repository provides insights and examples for Accounting, Customer, Employee, Legal, Management, and Operations, making it a valuable reference for professionals and researchers. It details specific projects such as predictive modeling with GitHub logs, satellite data analysis for financial forecasting, and data imputation techniques. The resource also highlights opportunities for collaboration with Sov.ai, a company focused on integrating advanced machine learning with financial data analysis, and includes a wide range of research and project opportunities.

SearchGPT offers a unique AI-powered spreadsheet generator, allowing users to create custom spreadsheets simply by entering a text prompt. This tool is designed to simplify data organization and analysis for a wide range of applications. Users can describe the type of spreadsheet they need, including columns, data types, and specific instructions, and SearchGPT will generate it. More complex spreadsheet requests may take up to a minute to process, indicating its capability to handle detailed instructions. The platform emphasizes ease of use, making it accessible for individuals who need quick data structuring without manual setup.

DoclingConverter is a Hugging Face Space designed to streamline the process of converting PDF documents into more structured and editable formats like Markdown or JSON. Users can upload a PDF file and then select their desired output format. The tool not only extracts the textual content but also captures relevant metadata, making it highly useful for various applications. This simplifies document processing for tasks such as content management, data extraction, and archival. It is particularly beneficial for individuals and professionals who frequently work with PDFs and require an efficient way to transform them into machine-readable or easily editable formats.

AI Song and Lyrics Generator is a mobile application designed to empower users to effortlessly create original song lyrics and musical ideas. The tool utilizes smart AI technology to generate complete songs, including melodies, harmonies, and vocals, based on user-selected moods, themes, or keywords. This innovative approach significantly simplifies the songwriting process for both beginners and experienced content creators, enabling quick musical composition without requiring prior musical experience. Users can easily export their creations as high-quality MP3s, making it simple to share or further develop their musical projects. The app aims to democratize music creation by making advanced AI capabilities accessible to a broad audience.

SuperGlue Image Matching is an AI tool hosted on Hugging Face Spaces, designed for identifying corresponding features between different images. This capability is crucial for various computer vision tasks such as object recognition and visual localization. While the specific application details are not extensively provided on the live page, its presence on Hugging Face suggests it leverages advanced machine learning models for robust image analysis. The platform itself offers various pricing tiers for compute resources, allowing users to scale their usage based on their needs, from free CPU options to powerful GPU instances for more demanding tasks. This makes it accessible for both individual researchers and larger teams working on complex AI projects.

Health Prediction On Encrypted Data Using Fully Homomorphic Encryption is an innovative AI tool available on Hugging Face Spaces, designed to offer secure health predictions. It leverages fully homomorphic encryption (FHE) to analyze user-provided symptoms while keeping the data encrypted throughout the process. This ensures that sensitive health information remains private, addressing critical concerns in healthcare data security. Users can input at least five symptoms, encrypt them, and send them to the server for processing, receiving a disease diagnosis without compromising their privacy. This tool is particularly valuable for privacy-preserving machine learning (PPML) applications in the medical field, making advanced diagnostic capabilities accessible while maintaining data confidentiality.

SUPIR is an open-source project dedicated to developing practical algorithms for photo-realistic image restoration in real-world scenarios. It provides advanced capabilities for enhancing image quality, including super-resolution and the ability to handle various degradations. The project emphasizes achieving high generalization and image quality, with options for both quality-oriented and fidelity-oriented settings. Users can choose between different model versions (SUPIR-v0Q and SUPIR-v0F) depending on their specific needs, such as general high quality or better detail preservation for light degradations. An online demo, SupPixel AI, is also available for easy access to its cutting-edge AI technology for image processing and upscaling.

Monic was an AI-powered platform designed to enhance learning and study habits by providing a suite of intelligent tools. It featured an AI Summarizer for condensing information, a Flashcard Maker for creating custom study aids, a Quiz Generator for testing knowledge, and an AI Chat with Files for interactive learning. The platform aimed to transform how students learn and retain information, making the study process smarter and more effective. After two years and helping over 100,000 learners, Monic.ai closed its doors on March 7, 2026, with users able to request data exports until then. The team expressed gratitude for the community built and the impact made on student learning.

Fin Research is an AI tool designed to analyze sentiment from financial news data, providing valuable insights for investment research and market trend analysis. Unlike many tools, it doesn't require users to provide input, as it leverages pre-existing data from a database. The tool generates an interactive dashboard, allowing users to explore sentiment trends and patterns in financial news. This makes it particularly useful for financial analysts and investors who need to quickly grasp market sentiment without the hassle of data collection.

papers-notebook is an open-source GitHub repository dedicated to compiling reading notes on academic papers, primarily in the fields of distributed systems, virtualization, and machine learning. The project aims to document the author's thoughts on papers, including their core ideas, implementation methods, and personal evaluations, with each note ideally under 1000 words. While initially maintained as Markdown files, the project has transitioned to using GitHub issues for ongoing maintenance and welcomes community contributions for paper suggestions. The repository covers a wide range of topics within its focus areas, including schedulers like Mesos and Borg, consensus algorithms like Raft and Paxos, storage systems, virtualization technologies, and security aspects.

Apollo AI is the #1 AI academic researcher designed for students and academics. It allows users to instantly analyze PDFs of any length, summarize YouTube videos, and generate publication-ready charts and visualizations. The tool conducts deep web research with verified academic sources, identifies research gaps, and synthesizes findings into comprehensive reports with proper citations. Apollo AI also features real-time voice AI for hands-free research, multi-language support (including English, Arabic, Chinese, and French), and a literature review assistant to synthesize multiple research papers. It offers a generous free tier with core AI research features, making it accessible for a wide range of users.

deep-learning-with-python-notebooks is a GitHub repository containing Jupyter notebooks that implement the code samples from the book "Deep Learning with Python" by Francois Chollet and Matthew Watson. It includes notebooks for the third edition (2025), as well as legacy notebooks for the second (2021) and first (2017) editions. These notebooks focus on runnable code blocks and section titles, omitting text paragraphs, figures, and pseudocode, making them ideal for side-by-side use with the book. Users can run the code on Colab, which provides a hosted runtime with all necessary dependencies, or locally by setting up a Jupyter environment. The code for the third edition uses Keras 3 and supports JAX, TensorFlow, or PyTorch as backends. The repository also provides instructions for using Kaggle data and setting up Kaggle API keys as Colab secrets.

Infinilearn is a free fantasy RPG designed to help middle school students in grades 6-8 master math concepts. Students engage in battles by solving Common Core-aligned math problems covering topics like fractions, decimals, equations, geometry, and ratios. The game features adaptive difficulty, pulling problems from a student's weakest subjects for targeted practice. It runs in any modern web browser, requiring no downloads. While the game is completely free for students with no ads or in-app purchases, parents and teachers can opt for a premium subscription to access deeper analytics, Common Core standards tracking, and the ability to manage multiple children or classes. Infinilearn emphasizes a safe environment with no external links or personal information sharing, and uses preset quick-chat options for multiplayer interactions.

Data Phoenix is a comprehensive live media platform designed for AI and Data professionals. It offers a rich array of content including news, in-depth articles, digests, event listings, videos, and original shows. The platform focuses on covering technologies under the hood, best practices, and live demonstrations from industry builders. Users can stay updated on the latest advancements in AI and data, explore tech talks, demo stages, and research talks. Data Phoenix also provides opportunities to subscribe to newsletters, follow live streams, and partner with them to reach a large audience of AI and data professionals.

Facetorch App is a Python library designed for comprehensive facial analysis, available as a Hugging Face Space. It allows users to upload photos or use a webcam to detect faces, generate 3D facial landmarks, and analyze various facial attributes. The app provides detailed reports on detected facial expressions, action units, and emotion scores. It also includes capabilities for extracting facial embeddings and performing face recognition. This tool is particularly useful for developers and researchers in computer vision who require advanced facial analysis functionalities for their projects.

Deep-Learning-TensorFlow is a GitHub repository offering a collection of pre-built Deep Learning algorithms implemented with the TensorFlow library. This package is designed as a command-line utility, enabling users to quickly train and evaluate popular Deep Learning models. It can also serve as a benchmark or baseline for comparing custom models and datasets. The repository includes implementations for Convolutional Networks, Restricted Boltzmann Machines, Deep Belief Networks, Deep Autoencoders, Denoising Autoencoders, Stacked Denoising Autoencoders, and MultiLayer Perceptrons. It also supports Logistic Regression. The package can be installed via pip as 'yadlt' or by cloning the GitHub repository, and it features a scikit-learn-like interface for ease of use.

deep-learning-models is a GitHub repository offering Keras code and pre-trained weights for several widely used deep learning models. This resource includes implementations for VGG16, VGG19, ResNet50, Inception v3, and a CRNN for music tagging. The architectures are designed to be compatible with both TensorFlow and Theano backends, automatically adapting to the image dimension ordering specified in your Keras configuration. Users can easily load pre-trained weights, such as 'imagenet' for image models or 'msd' for the music tagging model, which are automatically downloaded and cached locally. While this repository is deprecated in favor of `keras.applications`, it remains a valuable reference for understanding and utilizing these foundational models.

machine-learning-cheat-sheet offers a comprehensive collection of classical equations and diagrams essential for understanding machine learning concepts. This resource is designed to help users quickly recall fundamental knowledge and ideas, making it particularly useful for students, professionals, and anyone preparing for job interviews in the machine learning field. The cheat sheet is available as a downloadable PDF, providing a convenient and accessible reference. It also includes instructions for compiling the LaTeX source on various platforms, catering to users who prefer to customize or build the document themselves.