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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.

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.

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.