TensorFlow's support for Deep Learning
Developers building model for Deep Learning now have some 15 major/minor frameworks to choose from. Such a wide set of choices reflects the level of interest in Deep Learning across academia and enterprises, but choosing an appropriate framework can be confusing given the trade-off among them. In this talk, we will take a close look at the capabilities of TensorFlow, the open source framework from Google that currently has the highest rate of adoption. We will cover the extensive language API's, high level API's, scaling the training across CPU and GPU with distributed mode, and the recently announced eager execution mode. We will also demonstrate the tools for debugging, visualization and model serving.