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 main library is suitable for a wide range of machine learning techniques, and not just for in-depth training.
- Linear algebra and other insides are clearly visible from the outside.
- In addition to the basic functionality of machine learning, TensorFlow also includes its own logging system, its own interactive log visualizer and even a powerful data delivery architecture.
- The TensorFlow performance model differs from the scicit-learn of the Python language and from most tools in R.
Check official website for more info.