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A resource repository for 3D machine learning
The main features of timzhang642/3d-machine-learning are: Awesome List, AI and Machine Learning, Computer Vision Research, Curated Research Lists, Machine Learning, Game Development Resources, Related Awesome Lists.
Projects with overlapping indexed features include: vinjn/awesome-d3d12 — Awesome D3D12 ecosystem. calinou/awesome-gamedev — A curated list of good stuff related to the development of games. This list contains only [free software][1] for code,… awesomedata/awesome-public-datasets — This project is a community-maintained, open-access directory of high-quality public datasets. It serves as a… fffaraz/awesome-cpp — This project is a comprehensive, curated directory of high-quality libraries, tools, and educational resources for C… ethicalml/awesome-production-machine-learning — A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning. christoschristofidis/awesome-deep-learning — This project is a curated directory of resources, libraries, and frameworks designed to support the development,…
A curated list of good stuff related to the development of games. This list contains only free software1 for code, sellers who aren't evil for physical resources, and free cultural works2 for assets.
This project is a community-maintained, open-access directory of high-quality public datasets. It serves as a centralized reference point for researchers, developers, and data scientists to locate reliable information sources across a wide spectrum of industries and scientific fields. By providing a structured index, the repository facilitates the discovery of data necessary for exploratory analysis, machine learning model training, and the development of data-intensive applications. The directory distinguishes itself through a lightweight, platform-agnostic approach to resource indexing that
This project is a curated directory of resources, libraries, and frameworks designed to support the development, training, and deployment of neural network models. It serves as a comprehensive guide for navigating the machine learning ecosystem, providing structured access to software utilities and research materials. The directory distinguishes itself by aggregating tools across the entire machine learning lifecycle, ranging from data management and experiment tracking to production-ready model deployment. It functions as a central hub for discovering both foundational academic research and
