Machine Learning with TensorFlow

• Set up basic and advanced TensorFlow installations
• Deep-dive into training, validating, and monitoring training performance
• Set up and run cross-sectional examples (images, time-series, text, and audio)
• Create pipelines to deal with real-world input data
• Set up and run cross domain-specific examples (economics, medicine, text classification, and advertising)
• Empower the reader to go from concept to a production-ready machine learning setup/pipeline capable of real-world usage

TensorFlow is an open source software library for numerical computation using data flow graphs. 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.

This book approaches common commercial machine learning problems using Google’s TensorFlow library. It will cover unique features of the library such as Data Flow Graphs, training, visualisation of performance with TensorBoard—all within an example-rich context using problems from multiple industries. The is on introducing new concepts through problems that are coded and solved over the course of each chapter.