Machine learning is one of the most fascinating and most important fields in modern technology. As I write this book, NASA has discovered faraway planets by using machine learning to analyze telescope images. After only three days of training, Google’s AlphaGo program learned the complex game of Go and defeated the world’s foremost master.

Despite the power of machine learning, few programmers know how to take advantage of it. Part of the problem is that writing machine learning applications requires a different mindset than regular programming. The goal isn’t to solve a specific problem, but to write a general application capable of solving many unknown problems.

Machine learning draws from many different branches of mathematics, including statistics, calculus, linear algebra, and optimization theory. Unfortunately, the real world doesn’t feel any obligation to behave mathematically. Even if you use the best mathematical models, you can still end up with lousy results. I’ve encountered this frustration on many occasions, and I’ve referred to neural networks more than once as “high-tech snake oil.”

TensorFlow won’t give you the ideal model for analyzing a system, but it will reduce the time and frustration involved in machine learning development. Instead of coding activation functions and normalization routines from scratch, you can access the many built-in features of the framework. TensorFlow For Dummies explains how to access these features and put them to use. ...

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