The tensorflow/tensorflow GitHub repository contains the core open-source library for TensorFlow, an end-to-end open-source platform for machine learning. This project is backed by Google and provides a comprehensive ecosystem of tools, libraries, and community resources that facilitate researchers and developers to easily build and deploy machine learning applications.

TensorFlow uses data flow graphs where nodes represent mathematical operations, while the edges represent the data (tensors) communicated between them. It's capable of running on multiple CPUs or GPUs, making it appropriate for a variety of tasks.

The TensorFlow repository also provides comprehensive documentation, which covers detailed instructions on getting started with TensorFlow, guides, tutorials and various machine learning resources.

Its features include:

1. Easy model building: Build and train ML models easily using intuitive high-level APIs like Keras with eager execution, which makes for immediate model iteration and easy debugging.

2. Robust ML production anywhere: Easily train and deploy models in the cloud, on-prem, in the browser, or on-device no matter what language you use.

3. Powerful experimentation for research: A flexible and powerful library for machine learning and numerical computation, with particular strengths in gradient-based machine learning algorithms.

Here is a basic code snippet on how it is constructed in Python. ```python import tensorflow as tf

Create a Constant op

const = tf.constant("Hello, TensorFlow!")

Start tf session

sess = tf.Session()

Run the op

print(sess.run(const)) ```

Please note, before starting, you need to install it via pip for Python. The common command would be pip install tensorflow. If you want the version that supports GPU, use pip install tensorflow-gpu.