Why do I start the TensorFlow.NET project?

In a few days, it was Christmas in 2018. I watched my children grow up and be sensible every day, and I felt that time passed too fast. IT technology updates are faster than ever, and a variety of front-end technologies are emerging. Big data, Artificial Intelligence and Blockchain, Container technology and Microservices, Distributed Computing and Serverless technology are dazzling. The Amazon AI service interface claims that engineers who don’t need any machine learning experience can use it, so that the idea of just calming down for two years and planning to switch to an AI architecture in the future is a splash of cold water.

TensorFlow is an open source project for machine learning especially for deep learning. It’s used for both research and production at Google company. It’s designed according to dataflow programming pattern across a range of tasks. TensorFlow is not just a deep learning library. As long as you can represent your calculation process as a data flow diagram, you can use TensorFlow for distributed computing. TensorFlow uses a computational graph to build a computing network while operating on the graph. Users can write their own upper-level models in Python based on TensorFlow, or extend the underlying C++ custom action code to TensorFlow.

In order to avoid confusion, the unique classes defined in TensorFlow are not translated in this book. For example, Tensor, Graph, Shape will retain the English name.