In this chapter, we are going to give an overview of one of the most widely used deep learning frameworks. TensorFlow has big community support that is growing day by day, which makes it a good option for building your complex deep learning applications. From the TensorFlow website:
"TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. TensorFlow was originally developed by researchers and engineers working on the Google Brain Team within Google's Machine Intelligence research organization for the purposes of conducting machine learning and deep neural networks research, but the system is general enough to be applicable in a wide variety of other domains as well."
The following topics are going to be covered in this chapter:
- TensorFlow installation
- The TensorFlow environment
- Computational graphs
- TensorFlow data types, variables, and placeholders
- Getting output from TensorFlow
- TensorBoard—visualizing learning