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Potato is an AI-driven scientific operating system for life sciences. It helps researchers design and execute experiments by synthesizing literature and generating lab protocols. Potato supports computational analyses, data workflows, and parallel experimental variations. It accelerates scientific execution for individual researchers, teams, and large organizations.

Applying_EANNs is a 2D Unity simulation where cars learn to navigate courses. The cars are controlled by a neural network trained using a genetic algorithm. This project demonstrates the application of evolutionary artificial neural networks in a simulated environment.

vowpal_wabbit is a machine learning system designed to push the frontier of machine learning. It incorporates techniques such as online learning and hashing. The system also supports reductions, learning2search, active, and interactive learning. It is available as an open-source tool.

SegLossOdyssey is a collection of loss functions for medical image segmentation. It provides various loss functions to improve the accuracy of image segmentation tasks. The tool is open-source and designed for researchers and practitioners in the field of medical imaging.

Agentarium is an open-source framework for creating and managing simulations populated with AI agents. It provides a platform for designing interactive environments where agents can act, learn, and evolve. Agentarium allows users to create, manage, and coordinate interactions between multiple AI agents.

flow-forecast is a deep learning PyTorch library for time series forecasting, classification, and anomaly detection. Originally designed for flood forecasting, it provides state-of-the-art models like transformers, attention models, and GRUs. It offers interpretability metrics, cloud provider integration, and model serving capabilities.