Research & Education

NXTLVL provides an AI-driven learning experience designed for children aged 7-11, focusing on developing essential problem-solving skills for the future. The platform offers online mini-missions led by a 'superhuman AI coach' that act as intellectual sparring partners, providing advice and helping children reflect on their actions. These 20-minute daily learning experiences are designed to be fun and effective, allowing kids to learn independently and level up their skills. Additionally, NXTLVL offers live team sessions facilitated by human instructors, where children collaborate to solve complex problems, enhancing their communication and collaboration skills. Parents can monitor progress through weekly AI Highlights and have the option to add 1:1 sessions with a human coach for deeper engagement. The program is developed by experts in education technology and includes a Problem Solving Olympiad for schools.

aima-exercises is an interactive and collaborative open-source platform designed to digitalize the exercises from the renowned textbook "Artificial Intelligence: A Modern Approach" by Stuart J. Russell and Peter Norvig. This platform serves as the exclusive online repository for exercises for the book's fourth edition, allowing students to solve them and teachers to contribute new ones. It leverages Jekyll 3 and Ruby 2.5, providing a structured environment with dedicated folders for figures, JavaScript codes, LaTeX files, and Markdown exercises. The project aims to foster a collaborative learning environment for AI education, making complex AI concepts accessible through practical exercises.

Applying_EANNs is a 2D Unity simulation designed to showcase how cars can learn to navigate various courses. The cars are controlled by a feedforward neural network, whose weights are optimized using a modified genetic algorithm. This project provides a practical demonstration of evolutionary artificial neural networks in a simulated environment. Users can tinker with simulation parameters in the Unity Editor or run the built executable with default settings. The neural network architecture includes an input layer, two hidden layers, and an output layer, with its training managed by a customizable genetic algorithm. The user interface displays real-time data for the best performing car, including neural network output, evaluation value, and a generation counter, along with a visual representation of the neural network's weights.

Betina is an AI-powered pet care specialist designed to provide immediate guidance on pet health, behavior, nutrition, and overall well-being. It aims to eliminate the need for constant vet visits for minor concerns by offering 24/7 support. Users can create a profile for their pet to receive personalized advice, ask questions via text or photo, and engage in follow-up discussions with Betina for comprehensive care. The tool also offers tailored pet training plans, from basic obedience to advanced tricks, along with expert advice and feedback to help strengthen the bond between pets and owners. Betina helps pet owners understand their pets' complex behaviors and needs through advanced AI, offering instant answers and personalized insights.

Awesome-Adaptation-of-Agentic-AI is a curated repository featuring a comprehensive list of academic papers focused on the adaptation strategies of agentic AI systems. This resource is designed for researchers and practitioners interested in the evolving field of agentic AI, offering insights into various adaptation methods. The repository categorizes papers based on agent adaptation (tool execution signaled, agent output signaled) and tool adaptation (agent-agnostic, agent-supervised), detailing development timelines, methods, venues, tasks, tools, agent backbones, and tuning techniques. It serves as a valuable reference for understanding the latest advancements and research trends in making AI agents more adaptive and intelligent.

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.

C-Plus-Plus is an open-source repository on GitHub providing a comprehensive collection of algorithms implemented in C++. Designed for educational purposes, it covers a wide range of topics including mathematics, machine learning, computer science, and physics. The repository features well-documented source code with detailed explanations, making it a valuable resource for both educators and students. Each algorithm implementation is atomic, utilizing STL classes without external library dependencies, which allows for in-depth study of the fundamentals. The code adheres to the C++17 standard, ensuring portability across various operating systems and embedded systems like ESP32 and ARM Cortex. It also includes self-checks for implementation correctness and modular designs for easy integration into other applications. Online documentation is generated directly from the source code, offering snippets, execution details, diagrams, and links to C++ STL library functions.

This GitHub repository, machine-learning-for-software-engineers, offers a detailed, multi-month study plan designed for software engineers looking to transition into machine learning engineering. The plan emphasizes a top-down, results-first approach, focusing on practical application and abstracting much of the underlying mathematics for beginners. It includes a curated list of video resources, books (beginner and practical), MOOCs, Kaggle competitions, and communities. The resource also provides insights into prerequisite knowledge, daily study routines, and motivation, making it a comprehensive guide for self-taught learners aiming for a career in ML.

mdlm is an open-source masked discrete diffusion language model (MDLM) that features a novel substitution-based parameterization. This approach simplifies the absorbing state diffusion loss to a mixture of classical masked language modeling losses, leading to state-of-the-art perplexity numbers on LM1B and OpenWebText among diffusion models. It also achieves competitive zero-shot perplexity with state-of-the-art autoregressive models on various datasets. The repository provides the MDLM framework, simplified loss calculation, baseline implementations, and efficient samplers that make MDLM significantly faster than existing diffusion models, including semi-autoregressive generation capabilities.

MiniWorld is a minimalistic 3D interior environment simulator specifically designed for reinforcement learning and robotics research. It allows users to simulate environments featuring rooms, doors, hallways, and various objects, making it suitable for tasks like training AI agents in office, home, or maze-like settings. Written 100% in Python, MiniWorld is easily modifiable and extensible, offering features such as few dependencies, good performance, lightweight design, and support for domain randomization for sim-to-real transfer. It also provides fully observable top-down views, depth map production, and the ability to display alphanumeric strings on walls. This project has been deprecated as of August 11, 2025, and is no longer receiving updates or support.

Machine-Learning-Books-With-Python is an open-source GitHub repository designed to assist individuals in mastering machine learning concepts using Python. It offers comprehensive chapter-by-chapter notes, practical exercises, and corresponding code implementations for a variety of machine learning books. This resource is ideal for students and developers looking to deepen their understanding and practical skills in machine learning. The repository aims to provide a structured learning path, allowing users to follow along with popular textbooks and apply their knowledge directly through coding examples and solutions. It serves as a valuable companion for self-study and academic courses.

DIPY (Diffusion Imaging in Python) is a comprehensive open-source Python library designed for the analysis of MR diffusion imaging and other 3D/4D+ medical images. It provides a robust set of generic methods for tasks such as spatial normalization, signal processing, machine learning, and statistical analysis. Beyond general medical image processing, DIPY specializes in computational anatomy, offering advanced techniques for diffusion, perfusion, and structural imaging. The library is intended for research purposes, with a clear disclaimer for clinical deployment. It supports installation via pip or conda and adheres to Scientific Python SPEC 0 for version compatibility, making it accessible for researchers and developers in the medical imaging field.

deep-learning-uncertainty is an open-source repository dedicated to predictive uncertainty estimation in deep learning models. It offers a comprehensive literature survey, detailed paper reviews, and experimental setups for various baseline methods. The repository also includes a collection of implementations, making it a valuable resource for researchers and engineers. This tool is designed to help users understand, quantify, and improve the reliability of predictions made by deep learning models, addressing critical aspects of model trustworthiness and robustness. It serves as a central hub for exploring established and emerging techniques in uncertainty quantification.

Domain Transfer Network (DTN) is a TensorFlow-based implementation for unsupervised cross-domain image generation. This tool enables users to transfer image characteristics from one domain to another, such as converting SVHN images to MNIST, without requiring paired training data. It is designed for researchers and developers interested in image synthesis and domain adaptation, providing a practical framework for experimenting with generative models. The repository includes Python scripts for dataset download, preprocessing, model pretraining, training, and evaluation, making it a comprehensive resource for those working with generative adversarial networks (GANs) and similar architectures.

faceID_beta is an open-source project available on GitHub that provides an implementation of iPhone X's FaceID technology. It leverages face embeddings and siamese networks, processing RGBD images for facial recognition. The project is primarily presented as a Jupyter Notebook file, with an automatically generated Python file also available. This makes it particularly suitable for developers and researchers interested in understanding and experimenting with advanced facial recognition techniques. The repository includes details on the implementation and encourages users to explore the notebook version for a clearer understanding of the code's structure and functionality.

DriveLM is an open-source project focused on advancing autonomous driving research through Graph Visual Question Answering (GVQA). It provides comprehensive datasets, DriveLM-Data, built upon nuScenes and CARLA, specifically designed for driving with language. The project also offers DriveLM-Agent, a VLM-based baseline approach for jointly performing GVQA and end-to-end driving. DriveLM serves as a main track in the CVPR 2024 Autonomous Driving Challenge, offering a baseline, test data, submission format, and evaluation pipeline. It addresses the community's challenges by providing a benchmark for driving with language, exploring embodied applications of LLMs/VLMs, and investigating closed-loop planning with language.

DRL is an open-source collection of educational resources focused on Deep Reinforcement Learning. Hosted on GitHub, it offers a comprehensive set of materials including detailed slides, informative lecture notes, and explanatory videos, many of which are in Chinese. The repository covers fundamental and advanced topics such as Value-Based Learning (Q-learning, Sarsa, Experience Replay), Policy-Based Learning (REINFORCE, A2C, TRPO), Actor-Critic Methods, and specialized areas like Multi-Agent Reinforcement Learning and Imitation Learning. It's an excellent resource for students and researchers looking to deepen their understanding of DRL concepts and algorithms.

Fundamentals-of-Deep-Learning-Book serves as the official code companion for the O'Reilly "Fundamentals of Deep Learning, Second Edition" book. This GitHub repository offers practical, PyTorch-based implementations of all algorithms presented in the book, making it an invaluable resource for those looking to apply deep learning concepts. The code is primarily provided as Google Colab notebooks, allowing users to run examples directly from the repository without extensive setup. Additionally, some examples include .py files for more convenient execution. It also archives code from the first edition, ensuring comprehensive coverage for different versions of the book. This resource is ideal for students and practitioners aiming to deepen their understanding of deep learning through hands-on coding.

Musicgen Negative Prompting is an AI tool hosted on Hugging Face Spaces, designed to enhance music generation through the use of negative prompts. This functionality allows users to define elements or characteristics they wish to exclude from the generated music, offering a refined level of control over the creative process. By specifying what the music should *not* sound like, users can more effectively steer the AI towards desired outcomes, making it a valuable resource for refining musical ideas and exploring new creative boundaries. The tool is currently experiencing a runtime error, preventing its full functionality.

GNNs-Recipe is a comprehensive study guide designed to help students and practitioners learn about Graph Neural Networks (GNNs). Hosted on GitHub, this resource offers a concise yet thorough overview of GNNs, covering foundational concepts, advanced topics, and practical applications. It includes a gentle introduction to GNNs, links to essential survey papers for a broader understanding, and recommendations for diving deeper into the subject with books and courses. The guide also points to valuable resources for staying updated with recent methods, paper implementations, benchmarks, and datasets, making it an invaluable tool for anyone looking to master GNNs.

mlcourse is a comprehensive collection of machine learning course materials hosted on GitHub, offering a wide range of resources for learning fundamental machine learning concepts and techniques. The repository includes detailed lectures, homework assignments with programming problems, and conceptual checks. Topics covered span supervised and unsupervised learning, model evaluation, regularization techniques like Lasso and Elastic Net, kernel methods, and Bayesian statistics. It also features discussions on advanced topics such as gradient boosting, backpropagation, and various optimization methods. The materials are suitable for students and aspiring data scientists looking to deepen their understanding of machine learning principles through practical exercises and theoretical explanations.

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.

Kansei.app redefines language learning by offering interactive, personalized conversations with AI companions. Users can practice Spanish, English, Italian, French, German, and Japanese anytime, anywhere, building confidence and overcoming speaking anxiety. The platform provides real-time feedback and corrections, along with practical, real-life conversation scenarios tailored to individual interests and goals. Kansei also offers conversation boosters and dedicated AI tutors for grammar, vocabulary, and pronunciation explanations, ensuring a comprehensive and engaging learning experience that adapts to the user's level.

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.