Research & Education

WiDiD offers an immersive learning platform, WiDiD Immersive, which leverages Virtual Reality and active pedagogy to develop skills. The platform functions as a Learning Management System (LMS) allowing for the deployment of ready-to-use training courses or custom VR modules. It supports various VR headsets and web formats, enabling centralized management of pedagogical content. Key features include unlimited practice tools, detailed progress tracking, and customizable solutions for training organizations, educational institutions, and businesses. WiDiD also provides consulting services and develops bespoke VR content, with a focus on practical, engaging, and measurable learning experiences.

JarvisIR is an AI-powered image restoration tool designed to enhance and improve the quality of digital images. Users can upload images suffering from common problems such as blur, darkness, or noise. The tool intelligently analyzes the uploaded image, identifies the specific issues, and then recommends and applies the most suitable restoration algorithms to address them. The result is a processed, restored version of the image, aiming to elevate its overall perception and clarity. While the current live website indicates a runtime error, the intended functionality is to provide an intelligent solution for various image restoration needs.

deep-learning-time-series is a comprehensive, open-source resource for anyone interested in applying deep learning to time series forecasting. This GitHub repository provides a curated list of state-of-the-art papers, code implementations, and experimental results. It covers a wide range of topics, from classic methods to deep learning approaches, and includes information on competitions and theoretical resources. The repository is continuously updated with new research from conferences like AAAI and ICLR, offering insights into various techniques such as Autoformers, N-BEATS, and attention-based models. It serves as an invaluable tool for researchers and practitioners looking to explore, implement, and stay current with the latest developments in time series forecasting using deep learning.

detrex is an open-source research platform designed for Transformer-based detection algorithms, built upon Detectron2 and borrowing design principles from MMDetection and DETR. It serves as a comprehensive toolbox for object detection, segmentation, pose estimation, and various visual recognition tasks. The platform emphasizes a modular design, allowing users to easily construct customized models, and offers strong baselines for Transformer-based detection models with optimized hyper-parameters. Key features include a LazyConfig System for flexible configuration and a lightweight training engine. detrex also provides extensive documentation, a model zoo, and supports a wide array of methods like DETR, Deformable-DETR, DINO, and MaskDINO, making it a valuable resource for researchers and developers in the field.

devops-roadmap is an open-source GitHub repository offering a detailed guide to DevOps methodology and a roadmap for developers in 2019. It explains what DevOps is, its goals, and benefits, such as faster time to market and reduced defects. The resource breaks down the steps of DevOps, from planning and coding to building, testing, packaging, releasing, operating, and monitoring. It also provides a technology roadmap, suggesting languages, source code management tools, databases, and other technologies to learn. Additionally, it includes sections on Big Data and Machine Learning concepts, along with recommended books for further learning in AI and software architecture.

Pakodemy is a comprehensive digital education platform designed to assist students in preparing for major Turkish exams like LGS, YKS, and KPSS. The platform features an extensive question library with hundreds of thousands of questions and solution videos from over 20 publishers. It leverages AI to provide a personalized learning experience, adapting question difficulty to the student's level. Key offerings include nationwide mock exams, live online classes, coaching services, and achievement-based videos. Students can also track their progress, create study plans, and engage in competitive duels. Pakodemy aims to make exam preparation efficient and engaging, offering a wide array of resources within a single application.

YB Inspire aims to strengthen Europe's position as a global leader in technology and sustainability by cultivating an open-innovation entrepreneurial ecosystem. The platform offers programs like "The 42MTRX" and "The AI Native Organisation" to guide startups from idea to funding-ready businesses, providing expert guidance, a global platform, and funding opportunities. For corporates and SMEs, YB Inspire facilitates innovation through its Innovation Framework Accelerator and leadership programs, helping them boost the ROI of innovation. Investors benefit from derisked investments and data-driven insights through systematic validation and scalability assessments of startups. YB Inspire connects innovators across Europe, offering hands-on support, workshops, and events to empower various sectors for a sustainable future.

World Labs is a spatial intelligence company focused on developing advanced AI models capable of perceiving, generating, reasoning, and interacting with the 3D world. Their primary product, Marble, allows users to create spatially consistent, high-fidelity, and persistent 3D environments from multimodal inputs like text, images, videos, or 360 panoramas. Users can precisely control 3D layouts, interactively edit specific elements, and expand or combine worlds to build larger, more immersive experiences. The platform supports versatile outputs, enabling downloads and exports in various 2D and 3D formats for seamless integration into existing workflows in fields such as art, film, gaming, AR/VR, robotics, and architecture.

MCPyLate is an AI server designed to perform searches using PyLate, specifically tailored for finding solutions to LeetCode problems. Users can enter a text query to search for relevant solutions and receive top results, complete with code snippets and associated scores. This functionality aims to assist developers and students in quickly accessing and understanding different approaches to coding challenges. The tool is hosted on Hugging Face Spaces, making it accessible for those looking for a specialized search engine for competitive programming and algorithm practice.

finetune-transformer-lm provides the code and model for the research paper "Improving Language Understanding by Generative Pre-Training." This open-source project is designed for researchers and developers interested in replicating and experimenting with the generative pre-training techniques described in the paper. Specifically, it includes an implementation for the ROCStories Cloze Test, allowing users to run experiments and analyze results. While the code is provided as-is with no expected updates, it serves as a valuable resource for understanding the foundational concepts of generative pre-training and language understanding models. The repository also notes that the code is currently non-deterministic due to various GPU operations, with a median accuracy slightly lower than the paper's reported single run.

HLearn is a high-performance machine learning library developed in Haskell, designed to offer both speed comparable to low-level languages like C/C++ and flexibility akin to high-level languages such as Python. It distinguishes itself by leveraging functional programming principles and the SubHask library for fast numerical computations. The library's design is deeply rooted in abstract algebra, utilizing concepts like homomorphisms, monoids, and Abelian groups to enable features such as parallel batch training, online training, fast cross-validation, and weighted data points. HLearn also incorporates a unique History monad for debugging optimization procedures without runtime overhead. While it's a research project aiming for an optimal interface, its current focus is on foundational algebraic structures rather than a broad range of popular machine learning techniques.

Publish Studio is an independent product studio dedicated to shaping internet products with a focus on strong identity, sharp execution, and modern polish. The studio works to bring digital products to life, emphasizing a refined and contemporary approach to development. While the specific AI tools or platforms used are not detailed, the studio's mission suggests a comprehensive approach to product creation, likely involving various stages from conceptualization to final deployment. Their expertise lies in crafting internet products that stand out through thoughtful design and efficient implementation.

KENLG-Reading is a comprehensive repository dedicated to knowledge-enhanced text generation, offering a meticulously curated reading list, tutorials, papers, codes, datasets, and leaderboards. It serves as an invaluable resource for researchers and practitioners in the field, providing a survey published in ACM Computing Survey'22. The repository is actively maintained and updated, ensuring access to the latest advancements and high-citation papers. It covers various aspects of text generation, including basic NLG papers, pretrained language models, controllable generation methods, and knowledge-enhanced techniques using knowledge bases, knowledge graphs, and grounded text.

machine_learning_derivation is an open-source GitHub repository offering comprehensive notes and derivations for various machine learning algorithms. It covers fundamental topics such as linear regression, support vector machines, dimensionality reduction, and probabilistic graphical models, including EM algorithm and Gaussian Mixture Models. The repository also delves into advanced concepts like variational inference, Markov Chain Monte Carlo sampling, and Kalman filtering. It is designed to support learning and research in machine learning, providing detailed explanations and mathematical derivations for each algorithm. The content is presented in PDF format, making it accessible for in-depth study and reference.

LinksUs is an AI-driven platform designed to bridge the gap between students seeking industry experience and companies looking for emerging talent. It enables businesses to post real-world tasks and short-term projects, providing undergraduates with valuable pre-industry exposure. The platform streamlines talent acquisition for companies by offering a cost-effective way to engage with a pool of skilled students. For students, LinksUs facilitates gaining practical experience, building professional connections, and potentially earning certifications, all while contributing to business productivity. It aims to simplify the hiring process for businesses and empower students with hands-on learning opportunities.

InstructIR is an AI tool designed for high-quality image restoration, guided by human-written instructions. Developed by mv-lab and presented at ECCV 2024, this model offers an all-in-one solution for various image degradation problems. Users can input an image along with natural language prompts to restore images from multiple degradation types, such as denoising, deraining, deblurring, dehazing, and low-light image enhancement. InstructIR has demonstrated state-of-the-art results, improving over previous all-in-one restoration methods. The project also provides a novel benchmark dataset for future research in text-guided image restoration. It offers a Hugging Face demo and Google Colab tutorial for easy access and experimentation, making advanced image restoration accessible through intuitive text commands.

Interpretable-ml-book is an open-source resource offering a detailed guide to interpretable machine learning. This book, available for free online, as an ebook, or in paperback, addresses the critical need for transparency in machine learning decisions. It introduces techniques to make black-box models more understandable, covering algorithms for simple interpretable models and methods for analyzing complex models. The resource is designed for machine learning practitioners, data scientists, statisticians, and stakeholders who need to trust and explain AI decisions. It aims to foster a future where machines can clearly articulate their reasoning, making the transition into an algorithmic age more human-centric.

Hibay is a mobile application designed to enhance English speaking proficiency through engaging AI-powered conversations. The tool provides a judgment-free environment for users to practice their English across more than 100 realistic scenarios, ranging from everyday discussions to specialized business English and IELTS preparation. Users benefit from immediate feedback on their pronunciation, grammar, and vocabulary, which helps in identifying areas for improvement. Additionally, Hibay offers tailored learning plans, enabling users to build confidence and fluency at their own pace. This comprehensive approach makes it an effective solution for anyone looking to significantly improve their spoken English.

Leaderboard serves as a robust and comprehensive benchmarking platform specifically designed for Automatic Speech Recognition (ASR). It addresses the critical need for measurable performance in ASR systems by offering three core components: a TestSet Zoo, a Model Zoo, and a Benchmarking Pipeline. The TestSet Zoo includes a wide range of academic and SpeechIO-curated datasets covering various speech recognition tasks and scenarios in both English and Chinese. The Model Zoo comprises a collection of commercial APIs and open-source models for comparison. The platform provides a simple and well-specified pipeline for data preparation, recognition, post-processing, and error rate evaluation, enabling researchers and developers to easily benchmark, reproduce, and examine ASR systems.

Unlost provides a structured program designed to assist students in navigating their post-school journey. Through 6 weekly one-on-one sessions with a trained near-peer facilitator, participants develop a clear and actionable plan for their future. The program focuses on creating concrete post-school plans, identifying contingency pathways, and fostering the confidence needed to pursue these goals. While the website content is minimal, the core offering revolves around personalized guidance and support for students transitioning out of school, aiming to reduce uncertainty and empower them with a strategic outlook.

LightNet is an open-source project offering a collection of light-weight neural networks specifically designed for semantic image segmentation. It focuses on achieving high segmentation accuracy while maintaining computational efficiency, making it suitable for embedded devices often found in autonomous driving systems. The repository includes implementations of several architectures such as MobileNetV2Plus, RF-MobileNetV2Plus, MobileNetV2Vortex, MobileNetV2Share, Mixed-scale DenseNet, SE-WResNetV2, and ShuffleNetPlus. These models incorporate techniques like Spatial-Channel Squeeze & Excitation (SCSE), Receptive Field Block (RFB), and Vortex Pooling. LightNet provides code in PyTorch and supports training and evaluation on Cityscapes and Mapillary Vistas Datasets, along with data augmentation using GANs.

FotonVR provides a comprehensive Virtual Reality in Education solution, specifically designed for K-12 schools. The platform offers a complete VR classroom setup, enabling immersive learning experiences across various subjects including Science, Mathematics, History, and STEM. With over 1200 curriculum-aligned VR modules, FotonVR aims to enhance student engagement and understanding. The content is suitable for grades up to 12, making it a versatile tool for different educational levels. FotonVR focuses on delivering an interactive and engaging learning environment through virtual reality technology, supporting educators in creating dynamic lessons.

Machine-Learning-for-Algorithmic-Trading-Second-Edition_Original is an open-source GitHub repository accompanying the "Machine Learning for Algorithmic Trading, Second Edition" book published by Packt. This comprehensive resource aims to show how machine learning can add value to algorithmic trading strategies in a practical yet comprehensive way. It covers a broad range of ML techniques, from linear regression to deep reinforcement learning, and demonstrates how to build, backtest, and evaluate trading strategies driven by model predictions. The repository contains over 150 notebooks that put the book's concepts, algorithms, and use cases into action, providing numerous examples for working with market, fundamental, and alternative data, training models, and designing trading strategies. It also includes applications replicating recently published research and uses the latest software versions like pandas 1.0 and TensorFlow 2.2.

machine-learning-interview is a GitHub repository offering an extensive collection of resources for individuals preparing for machine learning interviews. It features a minimum viable study plan, covering topics from LeetCode questions to advanced ML system design. The repository includes real interview experiences from FAANG, Snapchat, and LinkedIn, along with detailed guides on ML system design use cases like YouTube recommendations and ad click prediction. It also provides quizzes to test ML knowledge and links to a Machine Learning System Design book on Amazon, making it a valuable resource for job seekers in the ML field.