TensorFlow is a library that can be used to quickly build deep neural networks. In TensorFlow, the mathematical operations that we've covered thus far are expressed as nodes. The edges between these nodes are tensors, or multidimensional data arrays. TensorFlow can, given a neural network defined as a graph and a loss function, automatically compute gradients for the network and optimize the graph to minimize the loss function.

TensorFlow was released as an open source project by Google in 2015. Since then it has gained a very large following and enjoys a large user community. While TensorFlow provides APIs in Java, C++, Go, and Python, we will only be covering the Python API.  The Python API is used in this book because it's both the most commonly used, and the API most commonly used for the development of new models.

TensorFlow can greatly accelerate computation by performing those calculations on one or more Graphics Processing Units. The acceleration that GPU computation provides has become a necessity in modern deep learning.