TensorFlow gives us 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. There are other deep learning libraries available, but they are still very experimental by comparison.
A key feature of TensorFlow that we already noted in Chapter 13, Parallelizing Neural Network Training with TensorFlow, is its ability to work with single or multiple GPUs. This allows users to train machine learning models very efficiently on large-scale systems.
TensorFlow has strong growth drivers. Its development is funded and supported by Google, and so a large team of software engineers work on improvements continuously. TensorFlow also has strong support from open source developers, who avidly contribute and provide user feedback. This has made the TensorFlow library more useful to both academic researchers and developers in their industry. A further...
