Research & Education

awesome-production-machine-learning is a comprehensive, curated list of open-source libraries specifically designed to support the entire lifecycle of machine learning models in production. This resource is invaluable for machine learning engineers and developers looking to streamline their MLOps practices. It covers essential areas such as model deployment, performance monitoring, version control for models and data, and scaling machine learning systems to handle large datasets and high traffic. By providing a centralized collection of tools, it helps improve the reliability, efficiency, and maintainability of ML deployments, making it easier to manage complex production environments.

PLAN by ixigo is an AI-based trip planning tool designed to simplify travel arrangements and create custom itineraries effortlessly. Users can filter potential trips based on various criteria, including budget, desired travel month, and travel time. The platform also allows users to specify areas of interest such such as religious sites, cultural experiences, nature, food festivals, historical landmarks, shopping, beaches, mountains, and nightlife. PLAN by ixigo provides detailed information on various travel destinations, including estimated pricing per night, helping users organize their trips efficiently and discover new places like Yercaud, Denpasar, and Kasauli.

awesome-game-ai is an open-source repository offering a curated collection of resources for game AI, specifically focusing on multi-agent reinforcement learning. It covers both perfect and imperfect information games, categorizing materials by game type. The repository includes open-source projects, review papers, research papers, conference information, and competitions related to game AI. It highlights advancements in games like Starcraft, Dota 2, Go, Chess, and various card games, providing valuable insights for researchers and developers in the field. Contributions to the list are welcomed via pull requests.

Awesome-Deepfakes-Detection is a curated collection of resources dedicated to deepfake detection, hosted on GitHub. It serves as a valuable hub for researchers and practitioners by compiling an extensive list of datasets, academic papers, and code related to the identification and analysis of deepfakes. The repository is meticulously organized, categorizing resources by various detection methodologies such as spatiotemporal, frequency-based, generalization, and multi-modal approaches. It also includes information on deepfake detection competitions and tools, making it an indispensable reference for anyone working on combating synthetic media. The open-source nature of the repository encourages community contributions, ensuring it remains up-to-date with the latest advancements in the field.

awesome-detection-transformer is a curated collection of research papers focusing on the application of transformer models for object detection and segmentation in computer vision. The repository is organized by research fields, making it easy for researchers and practitioners to navigate and find relevant studies. It includes papers on various aspects such as DETR, open-vocabulary and multi-modal detection, 3D object detection, segmentation, and pose estimation. The project also lists useful toolboxes like detrex and mmdetection, which are dedicated to transformer-based object detectors. This open-source GitHub repository encourages contributions from the community to ensure its comprehensiveness and accuracy.

awesome-machine-learning-in-compilers is a comprehensive, curated list of research papers, datasets, and tools dedicated to the application of machine learning in compilers and program optimization. This GitHub repository serves as an invaluable resource for researchers, academics, and practitioners looking to explore and advance the field. It categorizes papers into key areas such as Survey, Iterative Compilation and Compiler Option Tuning, Instruction-level Optimisation, Parallelism Mapping and Task Scheduling, Languages and Compilation, Auto-tuning and Design Space Exploration, Code Size Reduction, Cost and Performance Models, Domain-specific Optimisation, Learning Program Representation, ML for Compilers and Systems Optimisation, and Memory/Cache Modelling/Analysis. Additionally, it provides links to relevant books, talks, tutorials, software, benchmarks, and datasets, making it a central hub for anyone interested in the synergy between machine learning and compiler technology.

Awesome-ml-privacy-attacks is a comprehensive, open-source repository dedicated to cataloging academic papers focused on privacy attacks against machine learning models. This resource is invaluable for researchers, academics, and security professionals seeking to understand and mitigate vulnerabilities in AI systems. The curated list covers various attack types, including membership inference, reconstruction, property inference, and model extraction. Where available, the repository also provides links to the authors' code implementations, enabling practical exploration and replication of the research. It serves as a central hub for staying updated on the evolving landscape of ML privacy and security.

AI IXX is a comprehensive AI innovation ecosystem designed to unite businesses, experts, and technologies. The platform offers a wide array of resources including on-demand AI courses for all skill levels, expert-led webinars, and an extensive collection of AI eBooks. Users can also connect with AI experts for personalized 1:1 coaching and consultancy, or participate in AI transformation workshops to kickstart their business's AI journey. Additionally, AI IXX features an AI tool scout with over 4000 tools and a maturity check to assess AI readiness, making it a complete solution for AI education and implementation.

Bag_of_Tricks_for_Image_Classification_with_Convolutional_Neural_Networks is an open-source project that provides a platform for experimenting with and implementing various training tricks to improve the accuracy of image classification using Convolutional Neural Networks (CNNs). Inspired by the paper "Bag of Tricks for Image Classification with Convolutional Neural Networks," this repository tests popular techniques such as Xavier initialization, warmup training, no bias decay, label smoothing, random erasing, linear scaling learning rate, and cosine learning rate decay. It uses the CUB_200_2011 dataset and a VGG16 network for experiments, offering a practical resource for researchers and developers looking to optimize their CNN models.

The Distributed AI Research Institute (DAIR) is an independent, globally distributed organization of academics, activists, and engineers dedicated to community-rooted research. DAIR aims to cut through AI hype, exposing the real harms of AI systems while imagining and building alternative technological futures centered on care, safety, and possibility. Their work is grounded in lived experience, ensuring research addresses real problems and benefits everyone. DAIR's research areas include using data for change, identifying AI harms, envisioning alternative tech futures, and developing governance frameworks for AI systems. They prioritize comprehensive, principled research and invest in the well-being of their researchers.

Crepe offers a robust implementation of character-level convolutional networks for text classification, built on Torch 7. This open-source project allows users to reproduce the experimental results from the "Character-level Convolutional Networks for Text Classification" article published in NIPS 2015. It includes data preprocessing scripts to convert CSV datasets into a Torch 7 binary format and a training program. The tool is designed for technical users and researchers, providing a foundation for advanced text classification tasks. While it requires a specific environment, including Torch 7 and potentially a powerful GPU, it serves as a valuable resource for understanding and applying character-level CNNs.

ZestScout is an AI tool that is currently in development, with new and exciting features in progress. The team is actively building the next chapter of ZestScout, focusing on content curation and post generation. While specific details about its capabilities are not yet available, the tool aims to help users create ready-to-publish content. Users are encouraged to check back soon for updates on its progress and release. The current website indicates a focus on future innovation in the AI content space.

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.

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.

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.

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.

Flag Learn is a free interactive geography and astronomy quiz game designed to help users master world flags, capitals, US states, and constellations. The platform offers a variety of engaging game modes, including Map Locator, Ultimate Mode, and PvP battles, allowing users to test their knowledge and compete with others. A daily Flagle challenge is also available to keep learning fresh and consistent. Users can track their progress, making it an effective tool for improving geography and astronomy knowledge in a fun and interactive way. The game is accessible and designed for a broad audience interested in educational quizzes.

Combiencafait is a comprehensive online platform offering over 60 free and up-to-date calculators and simulators for a wide range of daily needs. Users can find tools for financial planning, such as mortgage simulations, salary conversions (gross to net), and compound interest calculations. The platform also includes health-related tools like BMI calculators, as well as utilities for managing time, understanding legal fees, and even basic chemistry calculations. Designed for ease of use, Combiencafait ensures all calculations are reliable and updated for 2026, without requiring registration or tracking user data, making it a secure and anonymous resource for quick decision-making.

The 🐍💨 Data Contamination Database is a Hugging Face Space designed to help users identify and manage data contamination within datasets and models. This application provides functionalities to filter and view data specifically related to contamination. Users can input particular evaluation datasets and contaminated sources, and then select various options to exclude or analyze these issues. It serves as a crucial resource for AI researchers and data scientists aiming to ensure the integrity and reliability of their data, ultimately leading to more robust and accurate AI models. The tool is hosted on Hugging Face Spaces, making it accessible for a wide range of users.