flow-forecast

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.

tape is a set of tasks assessing protein embeddings across different domains of protein biology. It provides data, weights, and code for running benchmarks on trained protein embeddings. The tool includes a pretraining corpus, supervised downstream tasks, pretrained language model weights, and benchmarking code.

awesome-quantum-machine-learning is a curated list of quantum machine learning algorithms, study materials, libraries, and software. It provides resources for learning the basics of quantum machine learning. The list includes algorithms, study materials, projects, and descriptions of projects around the web.

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.

advanced_lane_detection is a computer vision project for detecting lanes in images. It utilizes techniques from the Udacity Self-Driving Car Nanodegree program. The project involves camera calibration, distortion correction, and lane identification. It is suitable for those learning or working on autonomous vehicle technology.