awesome-tensorflow
TensorFlow - A curated list of dedicated resources http://tensorflow.org
A curated bookmark list of TensorFlow tutorials, projects, tools, papers, videos, and community links, a well-organized directory for anyone exploring what has been built with TensorFlow, not a software package itself.
Awesome TensorFlow is a curated list — essentially a bookmarks page kept in a repository — that points to interesting things people have built with TensorFlow. It isn't software itself; it's a directory of links to other projects, tutorials, libraries, papers, videos, blog posts, books, and communities organized around Google's TensorFlow framework. The README describes TensorFlow itself briefly as an open source software library for numerical computation using data flow graphs, the engine many people use to build deep learning models (a kind of AI that learns patterns from data).
The list is organized into named sections so you can scan it by what you're looking for: Tutorials (introductions and courses, including beginner-friendly ones and university courses), Models/Projects (open implementations of specific research ideas like neural style transfer, image captioning, GAN-based image generation, and chatbots), Powered by TensorFlow (apps and products built on the framework), Libraries, Tools/Utilities, Videos, Papers, Blog posts, Community, and Books. Each entry is a short link with a one-line description.
You would land on this page if you are starting out with TensorFlow and want to find ready-made tutorials or example code, or if you already use it and want to discover existing projects or papers before reinventing them. It is also handy as a reference for researchers and educators who want to point students at vetted resources rather than search results.
Because this is a link collection rather than a piece of software, there is no real tech stack to speak of — the repository itself is a Markdown document. The full README is longer than what was provided.
Where it fits
- Find beginner-friendly TensorFlow tutorials and university course materials without searching the web.
- Discover existing open-source TensorFlow model implementations such as neural style transfer, chatbots, and image captioning before building from scratch.
- Locate vetted research papers, blog posts, and books to deepen TensorFlow knowledge systematically.