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.

Table of Contents:
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 function 106 Computing loss with maximum likelihood estimation 107 Putting theory into practice 108 Multinomial Logistic Regression: Classifying Data into Multiple Categories 110 The Modified National Institute of Science and Technology (MNIST) Dataset 110 Defining the model with the softmax function 113 Computing loss with cross entropy 114 Putting theory into practice 115 Chapter 7: Introducing Neural Networks and Deep Learning 117 From Neurons to Perceptrons 117 Neurons 118 Perceptrons 119 Improving the Model 121 Weights 121 Bias 122 Activation functions 123 Layers and Deep Learning 127 Layers 128 Deep learning 129 Training with Backpropagation 129 Implementing Deep Learning 131 Tuning the Neural Network 133 Input standardization 134 Weight initialization 135 Batch normalization 136 Regularization 139 Managing Variables with Scope 141 Variable scope 141 Retrieving variables from collections 142 Scopes for names and arguments 143 Improving the Deep Learning Process 143 Creating tuned layers 144 Putting theory into practice 145 Chapter 8: Classifying Images with Convolutional Neural Networks (CNNs) 149 Filtering Images 149 Convolution 150 Averaging Filter 151 Filters and features 152 Feature detection analogy 153 Setting convolution parameters 153 Convolutional Neural Networks (CNNs) 155 Creating convolution layers 156 Creating pooling layers 158 Putting Theory into Practice 160 Processing CIFAR images 160 Classifying CIFAR images in code 162 Performing Image Operations 166 Converting images 166 Color processing 169 Rotating and mirroring 170 Resizing and cropping 172 Convolution 174 Putting Theory into Practice 175 Chapter 9: Analyzing Sequential Data with Recurrent Neural Networks (RNNs) 179 Recurrent Neural Networks (RNNs) 180 RNNs and recursive functions 181 Training RNNs 182 Creating RNN Cells 183 Creating a basic RNN 185 Predicting text with RNNs 188 Creating multilayered cells 190 Creating dynamic RNNs 191 Long Short-Term Memory (LSTM) Cells 192 Creating LSTMs in code 194 Predicting text with LSTMs 196 Gated Recurrent Units (GRUs) 196 Creating GRUs in code 197 Predicting text with GRUs 198 Part 3: Simplifying and Accelerating Tensorflow 199 Chapter 10: Accessing Data with Datasets and Iterators 201 Datasets 201 Creating datasets 202 Processing datasets 208 Iterators 213 One-shot iterators 213 Initializable iterators 215 Reinitializable iterators 216 Feedable iterators 217 Putting Theory into Practice 218 Bizarro Datasets 221 Loading data from CSV files 222 Loading the Iris and Boston datasets 223 Chapter 11: Using Threads, Devices, and Clusters 225 Executing with Multiple Threads 226 Configuring a new session 226 Configuring a running session 228 Configuring Devices 229 Building TensorFlow from source 229 Assigning operations to devices 235 Configuring GPU usage 237 Executing TensorFlow in a Cluster 238 Creating a ClusterSpec 239 Creating a server 240 Specifying jobs and tasks 241 Running a simple cluster 244 Chapter 12: Developing Applications with Estimators 247 Introducing Estimators 248 Training an Estimator 248 Testing an Estimator 250 Running an Estimator 250 Creating Input Functions 251 Configuring an Estimator 252 Using Feature Columns 253 Creating and Using Estimators 256 Linear regressors 257 DNN classifiers 260 Combined linear-DNN classifiers 262 Wide and deep learning 263 Analyzing census data 264 Running Estimators in a Cluster 269 Accessing Experiments 270 Creating an experiment 271 Methods of the experiment class 272 Running an experiment 273 Putting theory into practice 274 Chapter 13: Running Applications on the Google Cloud Platform (GCP) 277 Overview 278 Working with GCP projects 278 Creating a new project 279 Billing 279 Accessing the machine learning engine 280 The Cloud Software Development Kit (SDK) 280 The gcloud Utility 281 Google Cloud Storage 283 Buckets 283 Objects and virtual hierarchy 285 The gsutil utility 286 Preparing for Deployment 290 Receiving arguments 290 Packaging TensorFlow code 291 Executing Applications with the Cloud SDK 293 Local execution 294 Deploying to the cloud 295 Configuring a Cluster in the Cloud 299 Setting the training input 300 Obtaining the training output 303 Setting the prediction input 304 Obtaining the prediction output 305 Part 4: The Part of Tens 307 Chapter 14: The Ten Most Important Classes 309 Tensor 309 Operation 310 Graph 310 Session 311 Variable 311 Optimizer 312 Estimator 312 Dataset 312 Iterator 313 Saver 313 Chapter 15: Ten Recommendations for Training Neural Networks 315 Select a Representative Dataset 315 Standardize Your Data 316 Use Proper Weight Initialization 316 Start with a Small Number of Layers 316 Add Dropout Layers 317 Train with Small, Random Batches 317 Normalize Batch Data 317 Try Different Optimization Algorithms 318 Set the Right Learning Rate 318 Check Weights and Gradients 318 Index 319