what is tensorflow_tutorials fr?

This repository is a collection of tutorials for TensorFlow, a popular library used to build machine-learning models. The tutorials were written in January 2016 by Parag K. Mital and cover a progression of topics from the very basics up to more advanced neural network architectures. Each tutorial comes as both a standalone Python script and as a Jupyter Notebook, which is an interactive document format that lets you run code in small steps and see results inline. The series starts with how TensorFlow is set up and how it handles computation, then moves through regression techniques, and then into various kinds of neural networks: convolutional networks for image recognition, autoencoders for compressing and reconstructing data, a denoising autoencoder, a variational autoencoder, and a residual network. The README includes links to installation guides for TensorFlow on different operating systems, as well as instructions for getting it running on cloud GPU instances on Amazon EC2 for those who need more computing power. Pre-compiled installation files for Ubuntu with Python 3 and CUDA support are included in the repository. This is a learning resource rather than a production library. It is aimed at people who want to understand how these machine-learning concepts work by reading and running real code examples. The repository reflects TensorFlow as it existed in early 2016, so some APIs and practices will differ from current versions of the framework.