Machine Learning with TensorFlow

Machine Learning with TensorFlow gives readers a solid foundation in machine-learning concepts plus hands-on experience coding TensorFlow with Python.

Machine Learning with TensorFlow gives readers a solid foundation in machine-learning concepts plus hands-on experience coding TensorFlow with Python. You’ll learn the basics by working with classic prediction, classification, and clustering algorithms. Then, you’ll move on to the money chapters: exploration of deep-learning concepts like autoencoders, recurrent neural networks, and reinforcement learning. Digest this book and you will be ready to use TensorFlow for machine-learning and deep-learning applications of your own.

TensorFlow, Google’s library for large-scale machine learning, simplifies often-complex computations by representing them as graphs and efficiently mapping parts of the graphs to machines in a cluster or to the processors of a single machine.

What’s Inside this book;

  • Matching your tasks to the right machine-learning and deep-learning approaches
  • Visualizing algorithms with TensorBoard
  • Understanding and using neural networks

A great guide to machine learning. It helped launch my third career!

William Wheeler, TEKsystems

Have you ever wondered if there are limits to what computer programs can solve? Nowadays, computers appear to do a lot more than unravel mathematical equations. In the last half-century, programming has become the ultimate tool to automate tasks and save time, but how much can we automate, and how do we go about doing so?

Can a computer observe a photograph and say, “Aha, I see a lovely couple walking over a bridge under an umbrella in the rain”? Can software make medical decisions as accurately as trained professionals can? Can software predictions about the stock market perform better than human reasoning? The achievements of the past decade hint that the answer to all these questions is a resounding yes, and the implementations appear to share a common strategy.

Recent theoretical advances coupled with newly available technologies have enabled anyone with access to a computer to attempt their own approach at solving these incredibly hard problems. Okay, not just anyone, but that’s why you’re reading this book, right?

A programmer no longer needs to know the intricate details of a problem to solve it. Consider converting speech to text: a traditional approach may involve understanding the biological structure of human vocal chords to decipher utterances by using many hand-designed, domain-specific, un-generalizable pieces of code. Nowadays, it’s possible to write code that looks at many examples and figures out how to solve the problem, given enough time and examples.

Algorithms learn from data, similar to the way humans learn from experience. Humans learn by reading books, observing situations, studying in school, exchanging conversations, and browsing websites, among other means. How can a machine possibly develop a brain capable of learning? There’s no definitive answer, but world-class researchers have developed intelligent programs from different angles. Among the implementations, scholars have noticed recurring patterns in solving these kinds of problems that has led to a standardized field that we today label machine learning (ML).

As the study of ML matures, the tools have become more standardized, robust, high-performing, and scalable. This is where TensorFlow comes in. This software library has an intuitive interface that lets programmers dive into using complex ML ideas. The next chapter presents the ins and outs of this library, and every chapter thereafter explains how to use TensorFlow for each of the various ML applications.


Author Nishant Shukla is a computer vision researcher focused on applying machine-learning techniques in robotics.

Senior technical editor, Kenneth Fricklas, is a seasoned developer, author, and machine-learning practitioner.

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