Research & Education

rl-book offers the complete source codes for the book "Reinforcement Learning: Theory and Python Implementation." This resource provides a tutorial approach to reinforcement learning, detailing both theoretical concepts and practical Python implementations. It features one-to-one mapping between theory and code, supporting TensorFlow 2 and PyTorch 1&2. The implementations cover a wide range of algorithms, from classic methods like SARSA and Q-Learning to modern deep reinforcement learning techniques such as PPO, DDPG, and SAC. All codes are designed for compatibility across Windows, Linux, and macOS, and can be run on a laptop without requiring a GPU for most examples. The project also includes supporting content like exercise answers and errata for both English and Chinese versions of the book.

SINet is an open-source project for Camouflaged Object Detection (COD), a challenging computer vision task focused on detecting objects that blend into their natural habitat. Developed by Deng-Ping Fan and colleagues, SINet was presented at CVPR 2020 (Oral) and offers a robust baseline for COD research. The repository includes detailed introductions, the Search & Identification Net (SINet) model, and one-key evaluation codes. It also features the COD10K dataset, which provides diverse and meticulously annotated samples for training and testing. SINet is implemented in PyTorch and supports both training and testing, with an enhanced version (SINet-V2) accepted at IEEE TPAMI 2022. The project also highlights potential applications in medical imaging, agriculture, art, and computer vision.

SUSTechPOINTS, hosted on GitHub, provides a comprehensive platform for software development, offering various plans tailored for individuals and organizations. The Free plan includes unlimited public/private repositories, Dependabot security updates, 2,000 CI/CD minutes/month, and 500MB of Packages storage. The Team plan expands on this with access to GitHub Codespaces, repository rules, multiple reviewers in pull requests, and increased CI/CD minutes and package storage. For larger organizations, the Enterprise plan adds advanced security, compliance features like SOC1/SOC2 reports, data residency options, and extensive support, making it suitable for managing complex projects and teams.

This project offers a comprehensive stock market environment built with OpenAI Gym, designed for simulating stock trading strategies using reinforcement learning. It integrates both Deep Q-learning and Policy Gradient algorithms, allowing users to experiment with advanced AI techniques in a financial context. The tool is implemented using Keras and supports various training data, although sample data provided is for Korean stocks. It emphasizes flexibility, encouraging users to modify model architectures and features to develop their own optimized solutions. This makes it an ideal platform for researchers and developers looking to explore and refine AI-driven trading strategies.

splatviz is a comprehensive, open-source Python-based interactive viewer designed for real-time editing and analysis of 3D Gaussian Splatting scenes. Utilizing the pyimgui GUI library, it enables direct manipulation of Gaussian Python objects just before rendering, offering extensive editing and visualization capabilities. Users can view multiple scenes simultaneously, either side-by-side or in a split-screen view, and evaluate Python expressions on the resulting scene. Key features include an Edit Widget for real-time manipulation of Gaussian parameters, an Eval Widget for debugging and visualizing variables, and a Camera Widget with Orbit and WASD modes for flexible scene navigation. It also supports attaching to running 3DGS training sessions for live inspection and editing.

TSFpaper is an open-source GitHub repository dedicated to providing a curated reading list of academic papers focused on Time Series Forecasting (TSF) and Spatio-Temporal Forecasting (STF). The repository organizes these papers by their respective model types, making it easier for users to navigate and find relevant research. It serves as a valuable resource for researchers, academics, and practitioners who are interested in staying updated with the latest advancements in these specialized forecasting domains. The collection aims to streamline the process of discovering key literature, fostering knowledge sharing within the scientific community.

UNeXt-pytorch is the official PyTorch implementation of UNeXt, an MLP-based network specifically designed for rapid medical image segmentation. This tool is ideal for researchers and developers working on medical imaging tasks, particularly those requiring quick processing for point-of-care applications. Based on a MICCAI 2022 paper, it offers a robust and efficient solution for segmenting medical images. The open-source nature of the project, hosted on GitHub, allows for community contributions and flexible integration into existing workflows, providing a strong foundation for advanced medical image analysis.

UniDet is an open-source object detection tool designed to operate across multiple large-scale datasets with an automatically learned unified label space. It was the winning solution of the ECCV 2020 Robust Vision Challenges. The tool offers state-of-the-art performance on datasets such as COCO, Objects365, OpenImages, and Mapillary. A key feature is its ability to predict class labels within this unified space, allowing it to be directly used for testing on novel datasets not included in its training. The repository also provides state-of-the-art baselines for Objects365 and OpenImages. UniDet is built on detectron2, making its inference API familiar to users of that framework.

Sweet Justice AI is a platform designed to help users find and access OnlyFans Telegram channels. The website boasts a collection of over 150,000 verified creators, providing a wide range of content. Users can browse various categories and get free preview content that is updated daily. The tool aims to offer exclusive access to popular groups and links across different messaging platforms, including Telegram, Discord, WhatsApp, and Messenger. While the name suggests an AI legal assistant, the actual content of the website is focused on adult content discovery, specifically OnlyFans leaks and related groups.

Web Bench Leaderboard is a comprehensive Data & Analytics tool hosted on Hugging Face Spaces, designed for evaluating and comparing language models. Users can access a dynamic leaderboard to view existing evaluations, filter data, and select specific columns to display relevant information about various models. The platform also enables users to submit their own evaluations, contributing to a growing dataset for performance analysis. This tool is ideal for researchers, data scientists, and anyone interested in monitoring and benchmarking the capabilities of AI language models.

YOLO26 vs RF-DETR is a Hugging Face Space designed for comparing the performance of two prominent object detection and segmentation models: YOLO26 and RF-DETR. Users can upload an image and then choose between detection or segmentation tasks. The tool provides options to adjust settings such as confidence threshold and model size, allowing for a detailed analysis of how each model performs under different conditions. This application is particularly useful for AI researchers and computer vision developers who need to benchmark and understand the nuances of these models in a practical, visual environment.

Smart-money-concepts is a Python package designed for algorithmic trading, integrating Inner Circle Trader (ICT) concepts into Python. It provides a suite of indicators such as Fair Value Gap (FVG), Swing Highs and Lows, Break of Structure (BOS) & Change of Character (CHoCH), Order Blocks (OB), and Liquidity. The package also includes functionalities to identify previous highs and lows across different timeframes and to analyze session-specific market activity and retracements. This tool is intended for traders and investors seeking to gain deeper insights into market sentiment, trends, and potential reversals through programmatic analysis.

Tutur is an AI language tutor designed to enhance language proficiency through interactive conversations and comprehensive assessments. The platform focuses on delivering personalized language learning experiences, adapting to individual user needs and progress. By engaging users in conversational practice, Tutur helps to build fluency and confidence in a new language. Its detailed assessment features provide insights into areas for improvement, allowing for a more targeted and effective learning journey. This tool is ideal for anyone looking to improve their language skills with a tailored and adaptive approach.

Smriti.co is a forthcoming AI tool that is currently in its pre-launch phase. The website indicates that it will be launching soon and provides a contact form for users to get in touch. Visitors can sign up for an email list to receive updates, promotions, and other information regarding the tool's release and features. The site is protected by reCAPTCHA and includes standard copyright information, suggesting a professional and legitimate upcoming service. While specific functionalities are not yet detailed, the platform is positioning itself to offer AI-driven solutions.

3D-Occupancy-Perception is a comprehensive research resource dedicated to the field of 3D dense perception for autonomous driving. This active repository provides a systematic survey of the latest advancements, encompassing LiDAR-Centric, Vision-Centric, and Multi-Modal Occupancy Perception. It delves into core methodological issues, including network pipelines, multi-source information fusion, and effective network training. The resource also offers evaluations, detailed performance comparisons, and discussions on current limitations and future research directions. It aims to inspire further research and development in the autonomous driving community by curating and highlighting significant works in the domain.

AIPND is a comprehensive repository designed to support the AI Programming with Python Nanodegree program. It contains a variety of tutorial notebooks and programming labs that supplement the course lessons. Users can explore topics such as Linear Algebra Essentials, including vectors, linear combinations, and linear mappings. The repository also features programming labs like the Intro to Python Lab for classifying images, and mini-projects utilizing NumPy and Pandas for data manipulation and analysis. Additionally, it includes practice exercises for Matplotlib and notes on challenging quiz concepts, making it a valuable resource for students looking to deepen their understanding and practical skills in AI programming with Python.

Unsupervised-Classification is a GitHub repository offering a PyTorch implementation of the paper "SCAN: Learning to Classify Images without Labels." This tool addresses the challenge of automatically grouping images into semantically meaningful clusters when ground-truth annotations are absent. It deviates from recent end-to-end approaches by advocating a two-step method where feature learning and clustering are decoupled. The project demonstrates significant performance improvements over state-of-the-art methods on various benchmarks, including CIFAR10, CIFAR100-20, STL10, and ImageNet. It provides code for pretext tasks (like SimCLR), clustering (SCAN), and self-labeling steps, along with pretrained models and evaluation scripts, making it a valuable resource for researchers in computer vision and unsupervised learning.

AiiDA (Automated Interactive Infrastructure and Database for computational science) is a powerful open-source workflow manager designed for computational science. It emphasizes robust data provenance tracking, high performance, and extensibility, allowing researchers to manage complex computational workflows efficiently. Key features include the ability to write complex, auto-documenting workflows in Python, an event-based workflow engine supporting thousands of processes per hour with full checkpointing, and automatic tracking of inputs, outputs, and metadata for full reproducibility. AiiDA also offers a flexible HPC interface compatible with various schedulers like SLURM and PBS Pro, a plugin interface for extending functionality with new simulation codes and data types, and tools for open science, enabling the export and sharing of provenance graphs.

Algorithmic-trading-python is a comprehensive open-source repository designed to accompany freeCodeCamp's YouTube course on algorithmic trading in Python. It offers practical resources for individuals looking to understand and implement algorithmic trading strategies. The repository guides users through fundamental concepts, API basics, and the development of various trading models. Key sections include building an equal-weight S&P 500 index fund, as well as quantitative momentum and value investing strategies. This resource is ideal for students and developers who want to gain hands-on experience in financial programming and automated trading.

Algorithmic Trading with Python is a GitHub repository containing the complete source code for the 2020 book by Chris Conlan. This resource is invaluable for researchers and developers interested in algorithmic trading, providing practical Python implementations of key concepts. It includes stand-alone scripts for performance metrics to evaluate trading strategies, common technical indicators implemented in pure Pandas, and methods for converting these indicators into ternary signals. The repository also features a generic grid search wrapper for numeric optimization, object-oriented building blocks for portfolio simulation, and a generic wrapper for multi-core repeated K-fold cross-validation. Additionally, it offers free-to-use simulated End-of-Day stock data and alternative data streams, making it a comprehensive toolkit for learning and applying algorithmic trading principles.

AliceVision is an open-source photogrammetric computer vision framework designed for 3D reconstruction and camera tracking. It provides a robust software foundation with state-of-the-art computer vision algorithms that can be tested, analyzed, and reused. The project is a collaborative effort between academia and industry, ensuring cutting-edge algorithms meet the quality and robustness required for production use. It allows users to infer the geometry of a scene from a set of unordered photographs or videos, effectively reversing the 3D scene to 2D projection process. The framework is primarily used through Meshroom, which offers both a user interface and a command-line tool for launching the AliceVision pipeline and customizing workflows with Python scripting.

Car Parking Puzzle Game is a mobile application designed to enhance strategic thinking and problem-solving skills through engaging car parking challenges. Players utilize intuitive hand gestures to navigate colorful vehicles through intricate parking lots, aiming to guide each car to its designated spot. With a variety of levels, it offers a casual yet addictive experience for puzzle enthusiasts seeking mental agility on their Android devices. This game is part of CityGamesOffline's collection, providing fully offline, lightweight games built for nonstop entertainment without the need for Wi-Fi. It promises smooth, lag-free gameplay even on low-end devices, with regular updates and new titles to keep the gaming library fresh.

Bus Fever - Car Parking Jam is a captivating mobile puzzle game developed by Microjoy Games Studio, designed to challenge your strategic thinking and provide a relaxing escape. The game blends Estonia's heritage of problem-solving with cutting-edge mobile game design, creating experiences that endure. Players untangle chaotic traffic by matching color-coded buses and passengers to clear congested parking lots. It offers an engaging experience for puzzle enthusiasts looking to sharpen their problem-solving skills on the go, with rigorously tested levels for balance, engagement, and technical precision to ensure flawless gameplay. The game features epic level challenges, mind-melting mini-games, and a core puzzle series based on classic 3-number matching mechanics.

The artificial-intelligence-for-trading GitHub repository serves as a comprehensive resource for Udacity's AI in Trading NanoDegree program. It contains practical code examples for various projects and quizzes, enabling students to apply AI concepts to financial market analysis and trading strategies. The repository is structured with dedicated folders for projects and quizzes, alongside helper files, requirements, and test functions to facilitate learning and development. While the data itself is not redistributable, the code provides a robust framework for understanding and implementing AI-driven trading solutions, making it an invaluable tool for those pursuing a career in quantitative finance or algorithmic trading.