The key features of TensorFlow

TensorFlow provides us with a scalable, multiplatform programming interface for implementing and running machine learning algorithms. The TensorFlow API has been relatively stable and mature since its 1.0 release in 2017, but it just experienced a major redesign with its recent 2.0 release in 2019, which we are using in this book.

Since its initial release in 2015, TensorFlow has become the most widely adopted deep learning library. However, one of its main friction points was that it was built around static computation graphs. Static computation graphs have certain advantages, such as better graph optimizations behind the scenes and support for a wider range of hardware devices; however, static computation graphs require separate graph declaration and graph evaluation steps, which make it cumbersome for users to develop and work with NNs interactively.

Taking all the user feedback to heart, the TensorFlow team decided to make dynamic computation...