Nick McClure - TensorFlow 2 Machine Learning Cookbook

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First published: February 2017
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Nick McClure is currently a senior data scientist at PayScale, Inc. in Seattle, WA. Prior to this, he has worked at Zillow Group and Caesar's Entertainment Corporation. He got his degrees in Applied Mathematics from The University of Montana and the College of Saint Benedict and Saint John's University.

He has a passion for learning and advocating for analytics, machine learning, and artificial intelligence. Nick occasionally puts his thoughts and musings on his blog, fromdata.org, or through his Twitter account, @nfmcclure.

Sujit Pal is a technology research Director at Elsevier Labs, an advanced technology group within the Reed-Elsevier Group of companies. His areas of interests include semantic search, natural language processing, machine learning and deep learning. At Elsevier, he has worked on several initiatives involving search quality measurement and improvement, image classification, duplicate detection, annotation and ontology development for medical and scientific corpora. He has co-authored a book on deep learning with Antonio Gulli and writes about technology on his blog Salmon Run. He has also worked as a technical reviewer on the book Reinforcement Learning in Python.

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TensorFlow was open source in November 2015 by Google, and since then it has become the most starred machine learning repository on GitHub. TensorFlow's popularity is due to its approach to creating computational graphs, automatic differentiation, and customizability. Because of these features, TensorFlow is a very powerful and adaptable tool that can be used to solve many different machine learning algorithms.

This book addresses many machine learning algorithms, applies them to real situations and data, and shows how to interpret the results.

This book is for the hobbyists, programmers, or enthusiasts who want both an introduction to TensorFlow and curated recipe examples for major machine learning algorithms. This book relies on basic knowledge of mathematics and Python programming. The major goal of this book is to introduce TensorFlow and provide well-documented examples of various machine learning algorithms. It is not within the scope of this book to delve into the specifics of mathematics, machine learning, or even programming. The best description of the book's target area is to give a gentle introduction to all three. Because of this, a reader with expertise in one area may find some parts of the book too slow and some parts too fast. Users with an extensive machine learning background may find the TensorFlow code enlightening, and users with an extensive Python programming background may find the explanations helpful. For the curious reader that would like more information on specific areas, there will be a