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Become a machine learning pro!
Google TensorFlow has become the darling of financial firms and research organizations, but the technology can be intimidating and the learning curve is steep. Luckily, TensorFlow For Dummies is here to offer you a friendly, easy-to-follow book on the subject. Inside, you'll find out how to write applications with TensorFlow, while also grasping the concepts underlying machine learning--all without ever losing your cool!
Machine learning has become ubiquitous in modern society, and its applications include language translation, robotics, handwriting analysis, financial prediction, and image recognition. TensorFlow is Google's preeminent toolset for machine learning, and this hands-on guide makes it easy to understand, even for those without a background in artificial intelligence.
Install TensorFlow on your computer
Learn the fundamentals of statistical regression and neural networks
Visualize the machine learning process with TensorBoard
Perform image recognition with convolutional neural networks (CNNs)
Analyze sequential data with recurrent neural networks (RNNs)
Execute TensorFlow on mobile devices and the Google Cloud Platform (GCP)
If you're a manager or software developer looking to use TensorFlow for machine learning, this is the book you'll want to have close by.
Auteur
Matthew Scarpino has been a programmer and engineer for more than 20 years. He has worked extensively with machine learning applications, especially those involving financial analysis, cognitive modeling, and image recognition. Matthew is a Google Certified Data Engineer and blogs about TensorFlow at tfblog.com.
Texte du rabat
Learn TensorFlow modules and create a neural network Discover the magic of machine learning TensorFlow, Google's free toolset for machine learning, has a huge following among corporations, academics, and financial institutions. With the guidance of this book, you can jump on board, too! TensorFlow For Dummies tames this sometimes intimidating technology and explains, in simple steps, how to write TensorFlow applications. Along the way, you'll get familiar with the concepts that underlie machine learning and discover some of the ways to use it in language generation, image recognition, and much more. Inside
Résumé
Become a machine learning pro!
Google TensorFlow has become the darling of financial firms and research organizations, but the technology can be intimidating and the learning curve is steep. Luckily, TensorFlow For Dummies is here to offer you a friendly, easy-to-follow book on the subject. Inside, you'll find out how to write applications with TensorFlow, while also grasping the concepts underlying machine learningall without ever losing your cool!
Machine learning has become ubiquitous in modern society, and its applications include language translation, robotics, handwriting analysis, financial prediction, and image recognition. TensorFlow is Google's preeminent toolset for machine learning, and this hands-on guide makes it easy to understand, even for those without a background in artificial intelligence.
Contenu
Introduction 1
About This Book 1
Foolish Assumptions 2
Icons Used in This Book 2
Beyond the Book 3
Where to Go from Here 4
Part 1: Getting to Know Tensorflow 5
Chapter 1: Introducing Machine Learning with TensorFlow 7
Understanding Machine Learning 7
The Development of Machine Learning 8
Statistical regression 9
Reverse engineering the brain 10
Steady progress 11
The computing revolution 12
The rise of big data and deep learning 12
Machine Learning Frameworks 13
Torch 14
Theano 14
Caffe 14
Keras 15
TensorFlow 15
Chapter 2: Getting Your Feet Wet 17
Installing TensorFlow 17
Python and pip/pip3 18
Installing on Mac OS 19
Installing on Linux 20
Installing on Windows 20
Exploring the TensorFlow Installation 21
Running Your First Application 22
Exploring the example code 23
Launching Hello TensorFlow! 23
Setting the Style 24
Chapter 3: Creating Tensors and Operations 27
Creating Tensors 27
Creating Tensors with Known Values 28
The constant function 30
zeros, ones, and fill 30
Creating sequences 31
Creating Tensors with Random Values 31
Transforming Tensors.33
Creating Operations 35
Basic math operations 35
Rounding and comparison 37
Exponents and logarithms 38
Vector and matrix operations 39
Putting Theory into Practice 42
Chapter 4: Executing Graphs in Sessions 45
Forming Graphs 46
Accessing graph data 47
Creating GraphDefs 49
Creating and Running Sessions 51
Creating sessions 51
Executing a session 52
Interactive sessions 53
Writing Messages to the Log 54
Visualizing Data with TensorBoard 56
Running TensorBoard 57
Generating summary data 57
Creating custom summaries 59
Writing summary data 59
Putting Theory into Practice 62
Chapter 5: Training 65
Training in TensorFlow 66
Formulating the Model 66
Looking at Variables 67
Creating variables 68
Initializing variables 68
Determining Loss 69
Minimizing Loss with Optimization 70
The Optimizer class 70
The GradientDescentOptimizer 71
The MomentumOptimizer 75
The AdagradOptimizer 76
The AdamOptimizer 77
Feeding Data into a Session 78
Creating placeholders 79
Defining the feed dictionary 79
Stochasticity 80
Monitoring Steps, Global Steps, and Epochs 80
Saving and Restoring Variables 82
Saving variables 82
Restoring variables 83
Working with SavedModels 84
Saving a SavedModel 85
Loading a SavedModel 86
Putting Theory into Practice 86
Visualizing the Training Process 89
Session Hooks 90
Creating a session hook 91
Creating a MonitoredSession 93
Putting theory into practice 94
Part 2: Implementing Machine Learning 97
Chapter 6: Analyzing Data with Statistical Regression 99
Analyzing Systems Using Regression 100
Linear Regression: Fitting Lines to Data 100
Polynomial Regression: Fitting Polynomials to Data 103
Binary Logistic Regression: Classifying Data into Two Categories 105
Setting up the problem 105 Defining models with the logistic f...