Machine Learning with TensorFlow

This book is for programmers who have some experience with Python and

linear algebra concepts like vectors and matrices. No experience with

machine learning is necessary.

Being able to make near-real-time decisions is becoming increasingly

crucial. To succeed, we need machine learning systems that can turn

massive amounts of data into valuable insights. But when you''re just

starting out in the data science field, how do you get started creating

machine learning applications? The answer is TensorFlow, a new open

source machine learning library from Google. The TensorFlow library

can take your high level designs and turn them into the low level

mathematical operations required by machine learning algorithms.

learning algorithms and how to implement solutions with TensorFlow.

It starts with an overview of machine learning concepts and moves on

to the essentials needed to begin using TensorFlow. Each chapter

zooms into a prominent example of machine learning. Readers can

cover them all to master the basics or skip around to cater to their

needs. By the end of this book, readers will be able to solve

classification, clustering, regression, and prediction problems in the

real world.

• Lots of diagrams, code examples, and exercises

• Solves real-world problems with TensorFlow

• Uses well-studied neural network architectures

• Presents code that can be used for the readers’ own applications

This book is for programmers who have some experience with Python and

linear algebra concepts like vectors and matrices. No experience with

machine learning is necessary.

Google open-sourced their machine learning framework called TensorFlow

in late 2015 under the Apache 2.0 license. Before that, it was used

proprietarily by Google in its speech recognition, Search, Photos, and

Gmail, among other applications. TensorFlow is one the most popular

machine learning libraries.