#algorithms

H2O-3 presents a powerful in-memory platform for distributed, scalable machine learning with user-friendly interfaces in R, Python, Scala, Java, and JSON. It seamlessly integrates with big data technologies such as Hadoop and Spark, offering support for popular algorithms including GLM, XGBoost, Random Forests, and Deep Learning. Its extensible architecture allows developers to integrate custom algorithms and data transformations. Export models for rapid scoring in production environments. Built upon the foundation of H2O-2, it ensures easy installation via PyPI and CRAN for Python and R users, broadening accessibility and usability across various platforms. Comprehensive documentation fosters user engagement and community contribution, while simplifying complex terminology for better understanding.

Explore a vast repository of JavaScript implementations for well-known algorithms and data structures, from basic to advanced levels. The collection covers topics such as linked lists, graphs, sorting, and dynamic programming, each accompanied by in-depth explanations and additional resources. This makes it an essential reference for learners and enthusiasts interested in deepening their skills in computational problem-solving.

Discover MarkLLM, a versatile open-source toolkit for watermarking large language models (LLMs), designed to verify text authenticity and origin. This toolkit features a range of algorithms, customization options, visualization capabilities, and thorough evaluation mechanisms, making it a valuable resource for researchers in AI and language model development.

DI-engine offers a versatile platform for reinforcement learning, integrating asynchronous-native task abstractions and core decision-making components such as Environment, Policy, and Model. Utilizing PyTorch and JAX, it supports a wide range of RL algorithms including basic, multi-agent, and model-based types. Targeted at both academic research and prototype development, it delivers modular tools and training resources, optimized for large-scale reinforcement learning tasks across various environments.

AI Explainability 360 offers a toolkit for interpreting AI models with algorithms such as LIME, SHAP, and ProtoDash. Supported data types include tabular, text, and image. Features include a detailed API and tutorials, encouraging community contributions. Suitable for developing robust AI explainability, it supports global and local contrastive methods, designed for extensibility.

d3rlpy provides cutting-edge offline and online deep reinforcement learning solutions with an intuitive API suitable for researchers and practitioners. Supporting distributional Q functions and data-parallel distributed training, it is designed for scenarios where online interaction is not feasible. Comprehensive documentation and tutorials assist users, regardless of their deep learning experience. Compatible with Linux, macOS, and Windows, d3rlpy ensures quality through rigorous benchmarking without exaggeration or undue complexity.

rPPG-Toolbox is an efficient open-source tool for remote photoplethysmography, facilitating quick algorithm development for camera-based physiological monitoring. Supporting leading neural and unsupervised methods, it suits diverse developmental requirements. The toolbox features a variety of supervised and traditional algorithms and integrates with seven datasets for extensive physiological research. It provides a simplified setup and uses pre-trained models to enhance data visualization. Key aspects include algorithm benchmarks and detailed configurations for training and testing, making it a valuable resource for researchers and developers in the physiological signal domain.

This project leverages machine learning and planning algorithms to assist with systematic solutions for math problems. Comprehensive documentation and interactive examples are accessible through Google Colab, promoting a practical learning experience. Tools such as Mathy Core for expression parsing and Mathy Envs for reinforcement learning environments assist educators and learners in finding innovative educational solutions. Continuous updates and community participation bolster the project's robustness.