Learning TensorFlow

Chapter 1. Introduction

This chapter provides a high-level overview of TensorFlow and its primary use: implementing and deploying deep learning systems. We begin with a very brief introductory look at deep learning. We then present TensorFlow, showcasing some of its exciting uses for building machine intelligence, and then lay out its key features and properties.

Going Deep

From large corporations to budding startups, engineers and data scientists are collecting huge amounts of data and using machine learning algorithms to answer complex questions and build intelligent systems. Wherever one looks in this landscape, the class of algorithms associated with deep learning have recently seen great success, often leaving traditional methods in the dust. Deep learning is used today to understand the content of images, natural language, and speech, in systems ranging from mobile apps to autonomous vehicles. Developments in this field are taking place at breakneck speed, with deep learning being extended to other domains and types of data, like complex chemical and genetic structures for drug discovery and high-dimensional medical records in public healthcare.

Deep learning methods—which also go by the name of deep neural networks—were originally roughly inspired by the human brain’s vast network of interconnected neurons. In deep learning, we feed millions of data instances into a network of neurons, teaching them to recognize patterns from raw inputs. The deep neural networks take raw ...