Result: Getting Started with TensorFlow

Title:
Getting Started with TensorFlow
Publisher Information:
Packt Publishing 2016
Document Type:
Electronic Resource Electronic Resource
Index Terms:
Availability:
Open access content. Open access content
copyrighted
Note:
English
Contributing Source:
CYBERLIBRIS
From OAIster®, provided by the OCLC Cooperative.
Accession Number:
edsoai.on1268805316
Database:
OAIster

Further Information

Get up and running with the latest numerical computing library by Google and dive deeper into your data!About This BookGet the first book on the market that shows you the key aspects TensorFlow, how it works, and how to use it for the second generation of machine learningWant to perform faster and more accurate computations in the field of data science? This book will acquaint you with an all-new refreshing library—TensorFlow!Dive into the next generation of numerical computing and get the most out of your data with this quick guideWho This Book Is ForThis book is dedicated to all the machine learning and deep learning enthusiasts, data scientists, researchers, and even students who want to perform more accurate, fast machine learning operations with TensorFlow. Those with basic knowledge of programming (Python and C/C++) and math concepts who want to be introduced to the topics of machine learning will find this book useful.What You Will LearnInstall and adopt TensorFlow in your Python environment to solve mathematical problemsGet to know the basic machine and deep learning conceptsTrain and test neural networks to fit your data modelMake predictions using regression algorithmsAnalyze your data with a clustering procedureDevelop algorithms for clustering and data classificationUse GPU computing to analyze big dataIn DetailGoogle's TensorFlow engine, after much fanfare, has evolved in to a robust, user-friendly, and customizable, application-grade software library of machine learning (ML) code for numerical computation and neural networks.This book takes you through the practical software implementation of various machine learning techniques with TensorFlow. In the first few chapters, you'll gain familiarity with the framework and perform the mathematical operations required for data analysis. As you progress further, you'll learn to implement various machine learning techniques such as classification, clustering, neural networks, and deep learning through practical