Fricklas Ken - Machine Learning with TensorFlow

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Like many people of my generation, Ive always been addicted to the latest online trends. Around 2005, I remember endlessly refreshing FARK, YTMND, and Delicious for entertainment and news. Now, I shuffle between Reddit and Hacker News, which led me to witness TensorFlows ceremonious debut on November 9, 2015. The post appeared at the top of the front page on Hacker News and received hundreds of commentsthat energy overshadowed anything else on the website.

At that time, machine-learning tools were already fragmented into a zoo of libraries; the ecosystem relied on experimental software packages from academic labs and proprietary solutions from industry giants. When Google revealed TensorFlow, the communitys responses were mixed. Despite Googles history of retiring beloved services (such as Google Reader, iGoogle, Knol, and Google Wave), the company also had a history of nurturing open source projects (such as Android, Chromium, Go, and Protobuf).

Bets had to be made right then and there about whether to adopt TensorFlow. Although many chose to wait until the library developed, a few dived right in. I sprinted through the official documentation, mastered the basics, and was ready to apply the technology to my doctoral research at UCLA. I accumulated notes diligently, having no idea that the pages I wrote for myself to navigate the TensorFlow documentation would develop into a book.

Around that time, an acquisitions editor at Manning Publications contacted me for a second opinion on a new Haskell bookpart of their due diligence procedure, because Im the author of Haskell Data Analysis Cookbook (Packt Publishing, 2014). The journey of writing the book youre reading right now began with my reply: On another note, have you heard about Googles new machine-learning library called TensorFlow?

Machine Learning with TensorFlow started with a traditional table of contents, featuring subjects you might expect in any machine-learning book, but it evolved to cover topics that lacked online tutorials. For example, its difficult to find online TensorFlow implementations of hidden Markov models (HMMs) and reinforcement learning (RL). Each iteration of editing the book introduced more concepts like these that didnt have sufficient existing sources.

Online TensorFlow tutorials are often too brief or too advanced for a beginner who wants to explore the art of machine learning. The purpose of this book is to fill those gaps, and I believe it does exactly that. If youre new to machine learning or TensorFlow, youll appreciate the books down-to-earth teaching style.

The deep gratification of writing this book traces back to the support of my family: Suman (Mom), Umesh (Dad), and Natasha (Sis). Their happiness and pride are always contagious.

Moral support throughout the months of writing came from my close college friends, the Awesomest Turntable DJs: Alex Katz, Anish Simhal, Jasdev Singh, John Gillen, Jonathon Blonchek, Kelvin Green, Shiv Sinha, and Vinay Dandekar.

Thank you, Barbara Blumenthal, my best friend and more, for tying the galaxies, nebulas, and whales with your pink ribbons. Youve been my escape, the cure to my writers block.

I would like to acknowledge the tremendous feedback Ive received from online communities: my posts on Reddit (r/artificial, r/machinelearning, r/Python, r/Tensor-Flow, and r/Programming) and Hacker News received fruitful attention. I thank those who posted on the official book forum and contributed to the GitHub repository. In addition, my thanks go to the amazing group of technical peer reviewers led by Aleksandar Dragosavljevi: Nii Attoh-Okine, Thomas Ballinger, John Berryman, Gil Biraud, Mikal Dautrey, Hamish Dickson, Miguel Eduardo, Peter Hampton, Michael Jensen, David Krief, Nat Luengnaruemitchai, Thomas Peklak, Mike Staufenberg, Ursin Stauss, Richard Tobias, William Wheeler, Brad Wiederholt, and Arthur Zubarev. Their contributions included catching technical mistakes, errors in terminology, and typos, and making topic suggestions. Each pass through the review process and each piece of feedback implemented through the forum topics shaped and molded the manuscript.

Special thanks go to Ken Fricklas, who served as the books senior technical editor; Jerry Gaines, the books technical development editor; and David Fombella Pombal, the books technical proofreader. They are the best technical editors I could have hoped for.

Finally, I want to thank the people at Manning Publications who made this book possible: publisher Marjan Bace and everyone on the editorial and production teams, including Janet Vail, Tiffany Taylor, Sharon Wilkey, Katie Tennant, Dennis Dalinnik, and many others who worked behind the scenes. Of all the interactions with the many individuals at Manning, I extend my greatest gratitude to Toni Arritola, the books development editor. Her persistent guidance and education throughout the process opened the book to a much wider audience.

Whether youre new to machine learning or just new to TensorFlow, this book will be your ultimate guide. Youll need working knowledge of object-oriented programming in Python to understand some of the code listings, but other than that, this book covers introductory machine learning from the basics.

The book is divided into three parts:

• tells you everything you need to know to begin using TensorFlow.
• discuss regression, classification, clustering, and hidden Markov models, respectively. Youll find these algorithms everywhere in the field of machine learning.