Language : English
Published : 2017-08-25
Pages : 242
Roughly inspired by the human brain, deep neural networks trained with large amounts of data can solve complex tasks with unprecedented accuracy. This practical book provides an end-to-end guide to TensorFlow, the leading open source software library that helps you build and train neural networks for computer vision, natural language processing (NLP), speech recognition, and general predictive analytics. Authors Tom Hope, Yehezkel Resheff, and Itay Lieder provide a hands-on approach to TensorFlow fundamentals for a broad technical audience–from data scientists and engineers to students and researchers. You’ll begin by working through some basic examples in TensorFlow before diving deeper into topics such as neural network architectures, TensorBoard visualization, TensorFlow abstraction libraries, and multithreaded input pipelines. Once you finish this book, you’ll know how to build and deploy production-ready deep learning systems in TensorFlow. Get up and running with TensorFlow, rapidly and painlessly Learn how to use TensorFlow to build deep learning models from the ground up Train popular deep learning models for computer vision and NLP Use extensive abstraction libraries to make development easier and faster Learn how to scale TensorFlow, and use clusters to distribute model training Deploy TensorFlow in a production setting
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How to make customers feel good about doing what you want Learn how companies make us feel good about doing what they want. Approaching persuasive design from the dark side, this book melds psychology, marketing, and design concepts to show why we’re susceptible to certain persuasive techniques. Packed with examples from every nook and cranny of the web, it provides easily digestible and applicable patterns for putting these design techniques to work. Organized by the seven deadly sins, it includes: Pride — use social proof to position your product in line with your visitors’ values Sloth — build a path of least resistance that leads users where you want them to go Gluttony — escalate customers’ commitment and use loss aversion to keep them there Anger — understand the power of metaphysical arguments and anonymity Envy — create a culture of status around your product and feed aspirational desires Lust — turn desire into commitment by using emotion to defeat rational behavior Greed — keep customers engaged by reinforcing the behaviors you desire Now you too can leverage human fallibility to create powerful persuasive interfaces that people will love to use — but will you use your new knowledge for good or evil? Learn more on the companion website, evilbydesign.info.
About the Author
Chris Nodder is an independent consultant with 20 years’ experience working with large organizations and lean startups to make user experience central to their business strategy. He was previously a director at the prestigious Nielsen Norman Group, and a senior user researcher at Microsoft. He has an MS in Human-Computer Interaction and a BS in Psychology.
This book covers the field of machine learning, which is the study of algorithms that allow computer programs to automatically improve through experience. The book is intended to support upper level undergraduate and introductory level graduate courses in machine learning.
The world of data warehousing has changed remarkably since the first edition of The Data Warehouse Lifecycle Toolkit was published in 1998. With this new edition, Ralph Kimball and his colleagues have refined the original set of Lifecycle methods and techniques based on their consulting and training experience. They walk you through the detailed steps of designing, developing, and deploying a data warehousing/business intelligence system. With substantial new and updated content, this second edition again sets the standard in data warehousing for the next decade.
About the Author
The authors’ professional careers have followed remarkably similar paths. Each author has focused on data warehousing and business intelligence (DW/BI) consulting and education for more than fifteen years. Most worked together at Metaphor Computer Systems, a pioneering decision support vendor, in the 1980s. All the authors are members of the Kimball Group and teach for KimballUniversity. They contribute regularly to Intelligent Enterprise magazine and other industry publications; most have previously written books in the Toolkit series. Ralph Kimball founded the Kimball Group. Since the mid 1980s, he has been the DW/BI industry’s thought leader on the dimensional approach and trained more than 10,000 IT professionals. Ralph has his Ph.D. in Electrical Engineering from Stanford University. Margy Ross is President of the Kimball Group. She has focused exclusively on DW/BI since 1982 with an emphasis on business requirements analysis and dimensional modeling. Margy graduated with a BS in Industrial Engineering from Northwestern University. Warren Thornthwaite began his DW/BI career in 1980. After managing Metaphor’s consulting organization, he worked for Stanford University and WebTV. Warren holds a BAin Communications Studies from the University of Michigan and anMBA from the University of Pennsylvania’sWharton School. JoyMundy has focused onDW/BIsystems since 1992 with stints at Stanford, Web TV, and Microsoft’s SQL Server product development organization. Joy graduated from Tufts University with a BA in Economics, and from Stanford University with an MS in Engineering Economic Systems. Bob Becker has helped clients across a variety of industries with their DW/BI challenges and solutions since 1989, including extensive work with health care organizations. Bob has a BSB in Marketing from the University of Minnesota’s School of Business.
Some books on algorithms are rigorous but incomplete; others cover masses of material but lack rigor. Introduction to Algorithms uniquely combines rigor and comprehensiveness. The book covers a broad range of algorithms in depth, yet makes their design and analysis accessible to all levels of readers. Each chapter is relatively self-contained and can be used as a unit of study. The algorithms are described in English and in a pseudocode designed to be readable by anyone who has done a little programming. The explanations have been kept elementary without sacrificing depth of coverage or mathematical rigor.The first edition became a widely used text in universities worldwide as well as the standard reference for professionals. The second edition featured new chapters on the role of algorithms, probabilistic analysis and randomized algorithms, and linear programming. The third edition has been revised and updated throughout. It includes two completely new chapters, on van Emde Boas trees and multithreaded algorithms, substantial additions to the chapter on recurrence (now called “Divide-and-Conquer”), and an appendix on matrices. It features improved treatment of dynamic programming and greedy algorithms and a new notion of edge-based flow in the material on flow networks. Many new exercises and problems have been added for this edition. As of the third edition, this textbook is published exclusively by the MIT Press. The hardcover edition does not include a dust jacket.
About the Author
Thomas H. Cormen is Professor of Computer Science and former Director of the Institute for Writing and Rhetoric at Dartmouth College. He is the coauthor (with Charles E. Leiserson, Ronald L. Rivest, and Clifford Stein) of the leading textbook on computer algorithms, Introduction to Algorithms (third edition, MIT Press, 2009). Charles E. Leiserson is Professor of Computer Science and Engineering at the Massachusetts Institute of Technology. Ronald L. Rivest is Andrew and Erna Viterbi Professor of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology. Clifford Stein is Professor of Industrial Engineering and Operations Research at Columbia University.