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The Resource Getting Started with TensorFlow, (electronic book)

Getting Started with TensorFlow, (electronic book)

Label
Getting Started with TensorFlow
Title
Getting Started with TensorFlow
Creator
Subject
Language
eng
Summary
  • 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 For This 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 Detail Google'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 examples. By the end of this book, you'll have gained hands-on experience of using TensorFlow and building classification, image recognition systems, language processing, and information retrieving systems for your application
Member of
Cataloging source
MiAaPQ
http://library.link/vocab/creatorName
Zaccone, Giancarlo
Dewey number
006.31
LC call number
Q325.5.Z33 2016
Literary form
non fiction
Nature of contents
dictionaries
http://library.link/vocab/subjectName
Machine learning
Label
Getting Started with TensorFlow, (electronic book)
Instantiates
Publication
Copyright
Carrier category
online resource
Carrier category code
cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
txt
Content type MARC source
rdacontent
Contents
  • Cover -- Copyright -- Credits -- About the Author -- About the Reviewer -- www.PacktPub.com -- Table of Contents -- Preface -- Chapter 1: TensorFlow - Basic Concepts -- Machine learning and deep learning basics -- Supervised learning -- Unsupervised learning -- Deep learning -- TensorFlow - A general overview -- Python basics -- Syntax -- Data types -- Strings -- Control flow -- Functions -- Classes -- Exceptions -- Importing a library -- Installing TensorFlow -- Installing on Mac or Linux distributions -- Installing on Windows -- Installation from source -- Testing your TensorFlow installation -- First working session -- Data Flow Graphs -- TensorFlow programming model -- How to use TensorBoard -- Summary -- Chapter 2: Doing Math with TensorFlow -- The tensor data structure -- One-dimensional tensors -- Two-dimensional tensors -- Tensor handling -- Three-dimensional tensors -- Handling tensors with TensorFlow -- Prepare the input data -- Complex numbers and fractals -- Prepare the data for Mandelbrot set -- Build and execute the Data Flow Graph for Mandelbrot's set -- Visualize the result for Mandelbrot's set -- Prepare the data for Julia's set -- Build and execute the Data Flow Graph for Julia's set -- Visualize the result -- Computing gradients -- Random numbers -- Uniform distribution -- Normal distribution -- Generating random numbers with seeds -- Montecarlo's method -- Solving partial differential equations -- Initial condition -- Model building -- Graph execution -- Computational function used -- Summary -- Chapter 3: Starting with Machine Learning -- The linear regression algorithm -- Data model -- Cost functions and gradient descent -- Testing the model -- The MNIST dataset -- Downloading and preparing the data -- Classifiers -- The nearest neighbor algorithm -- Building the training set -- Cost function and optimization
  • Testing and algorithm evaluation -- Data clustering -- The k-means algorithm -- Building the training set -- Cost functions and optimization -- Testing and algorithm evaluation -- Summary -- Chapter 4: Introducing Neural Networks -- What are artificial neural networks? -- [Neural network architectures] -- Neural network architectures -- Single Layer Perceptron -- The logistic regression -- TensorFlow implementation -- Building the model -- Launch the session -- Test evaluation -- Source code -- Multi Layer Perceptron -- Multi Layer Perceptron classification -- Build the model -- Launch the session -- Source code -- Multi Layer Perceptron function approximation -- Build the model -- Launch the session -- Summary -- Chapter 5: Deep Learning -- Deep learning techniques -- Convolutional neural networks -- CNN architecture -- TensorFlow implementation of a CNN -- Initialization step -- First convolutional layer -- Second convolutional layer -- Densely connected layer -- Readout layer -- Testing and training the model -- Launching the session -- Source code -- Recurrent neural networks -- RNN architecture -- LSTM networks -- NLP with TensorFlow -- Download the data -- Building the model -- Running the code -- Summary -- Chapter 6: GPU Programming and Serving with TensorFlow -- GPU programming -- TensorFlow Serving -- How to install TensorFlow Serving -- Bazel -- gRPC -- TensorFlow serving dependencies -- Install Serving -- How to use TensorFlow Serving -- Training and exporting the TensorFlow model -- Running a session -- Loading and exporting a TensorFlow model -- Test the server -- Summary -- Index
Control code
purchEBC4620789
Dimensions
unknown
Edition
1st ed.
Extent
1 online resource (178 pages)
Form of item
online
Isbn
9781786469069
Media category
computer
Media MARC source
rdamedia
Media type code
c
Reproduction note
Electronic resource.
Sound
unknown sound
Specific material designation
remote
Label
Getting Started with TensorFlow, (electronic book)
Publication
Copyright
Carrier category
online resource
Carrier category code
cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
txt
Content type MARC source
rdacontent
Contents
  • Cover -- Copyright -- Credits -- About the Author -- About the Reviewer -- www.PacktPub.com -- Table of Contents -- Preface -- Chapter 1: TensorFlow - Basic Concepts -- Machine learning and deep learning basics -- Supervised learning -- Unsupervised learning -- Deep learning -- TensorFlow - A general overview -- Python basics -- Syntax -- Data types -- Strings -- Control flow -- Functions -- Classes -- Exceptions -- Importing a library -- Installing TensorFlow -- Installing on Mac or Linux distributions -- Installing on Windows -- Installation from source -- Testing your TensorFlow installation -- First working session -- Data Flow Graphs -- TensorFlow programming model -- How to use TensorBoard -- Summary -- Chapter 2: Doing Math with TensorFlow -- The tensor data structure -- One-dimensional tensors -- Two-dimensional tensors -- Tensor handling -- Three-dimensional tensors -- Handling tensors with TensorFlow -- Prepare the input data -- Complex numbers and fractals -- Prepare the data for Mandelbrot set -- Build and execute the Data Flow Graph for Mandelbrot's set -- Visualize the result for Mandelbrot's set -- Prepare the data for Julia's set -- Build and execute the Data Flow Graph for Julia's set -- Visualize the result -- Computing gradients -- Random numbers -- Uniform distribution -- Normal distribution -- Generating random numbers with seeds -- Montecarlo's method -- Solving partial differential equations -- Initial condition -- Model building -- Graph execution -- Computational function used -- Summary -- Chapter 3: Starting with Machine Learning -- The linear regression algorithm -- Data model -- Cost functions and gradient descent -- Testing the model -- The MNIST dataset -- Downloading and preparing the data -- Classifiers -- The nearest neighbor algorithm -- Building the training set -- Cost function and optimization
  • Testing and algorithm evaluation -- Data clustering -- The k-means algorithm -- Building the training set -- Cost functions and optimization -- Testing and algorithm evaluation -- Summary -- Chapter 4: Introducing Neural Networks -- What are artificial neural networks? -- [Neural network architectures] -- Neural network architectures -- Single Layer Perceptron -- The logistic regression -- TensorFlow implementation -- Building the model -- Launch the session -- Test evaluation -- Source code -- Multi Layer Perceptron -- Multi Layer Perceptron classification -- Build the model -- Launch the session -- Source code -- Multi Layer Perceptron function approximation -- Build the model -- Launch the session -- Summary -- Chapter 5: Deep Learning -- Deep learning techniques -- Convolutional neural networks -- CNN architecture -- TensorFlow implementation of a CNN -- Initialization step -- First convolutional layer -- Second convolutional layer -- Densely connected layer -- Readout layer -- Testing and training the model -- Launching the session -- Source code -- Recurrent neural networks -- RNN architecture -- LSTM networks -- NLP with TensorFlow -- Download the data -- Building the model -- Running the code -- Summary -- Chapter 6: GPU Programming and Serving with TensorFlow -- GPU programming -- TensorFlow Serving -- How to install TensorFlow Serving -- Bazel -- gRPC -- TensorFlow serving dependencies -- Install Serving -- How to use TensorFlow Serving -- Training and exporting the TensorFlow model -- Running a session -- Loading and exporting a TensorFlow model -- Test the server -- Summary -- Index
Control code
purchEBC4620789
Dimensions
unknown
Edition
1st ed.
Extent
1 online resource (178 pages)
Form of item
online
Isbn
9781786469069
Media category
computer
Media MARC source
rdamedia
Media type code
c
Reproduction note
Electronic resource.
Sound
unknown sound
Specific material designation
remote

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