There are many other great deep learning frameworks out there. We chose Keras and TensorFlow primarily because of popularity, ease of use, availability for support, and readiness for production deployments. There are undoubtedly other worthy alternatives. 

Some of my favorites alternatives to TensorFlow include:

• Apache MXNet: A very high performance framework with a great new imperative interface called Gluon (https://mxnet.apache.org/)
• PyTorch: A very new and promising architecture originally developed by Facebook (http://pytorch.org/)
• CNTK: Microsoft's deep learning framework that can also be used with Keras (https://www.microsoft.com/en-us/cognitive-toolkit/)

While I do strongly believe that Keras and TensorFlow are the correct choices for this book, I also want to acknowledge these great frameworks and the contributions to the field that each project has made.