This book covers statistical regression and neural networks, visualizing machine learning with TensorBoard, performing image recognition with convolutional neural networks, analyzing sequential data with recurrent neural networks, and executing TensorFlow on mobile devices and Google Cloud.

TensorFlow For Dummies

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Preface

IN THIS CHAPTER Looking at machine learning over time Exploring machine learning frameworks 

Introducing Machine Learning with TensorFlow

IN THIS CHAPTER Obtaining and installing TensorFlow Exploring the TensorFlow package Running a simple application Understanding style conventions 

Getting Your Feet Wet

IN THIS CHAPTER Creating tensors with known and random values Calling functions that transform tensors Processing tensors with operations 

Creating Tensors and Operations

IN THIS CHAPTER Creating graphs and accessing their data Serializing data from a graph into a GraphDef Creating and launching sessions Printing messages to the log Visualizing summary data with TensorBoard 

Executing Graphs in Sessions

IN THIS CHAPTER Training applications in TensorFlow Creating variables and placeholders Minimizing loss with optimizers Splitting datasets into batches 

Training

IN THIS CHAPTER Identifying trends with linear and polynomial regression analysis Classifying points with logistic regression analysis Modeling systems with the logistic and softmax functions Computing loss with log likelihood and cross entropy 

Analyzing Data with Statistical Regression

IN THIS CHAPTER Exploring the development of neural networks Looking at perceptrons, multilayer perceptrons (MLPs), and deep learning Managing variables with scope Demonstrating deep learning in a TensorFlow application 

Introducing Neural Networks and Deep Learning

IN THIS CHAPTER Exploring image filtering and convolution Looking at convolutional neural networks (CNNs) Introducing the CIFAR-10 dataset Presenting TensorFlow’s image operations 

Classifying Images with Convolutional Neural Networks (CNNs)

IN THIS CHAPTER Analyzing sequential data with Recurrent Neural Networks (RNNs) Improving performance with Long Short-Term Memory (LSTM) Improving performance further with Gated Recurrent Units (GRUs) 

Analyzing Sequential Data with Recurrent Neural Networks (RNNs)

IN THIS CHAPTER Creating and manipulating datasets Loading and storing TFRecord data Exploring four types of iterators 

Accessing Data with Datasets and Iterators

IN THIS CHAPTER Configuring threads in applications Assigning operations to devices Creating distributed applications with clusters 

Using Threads, Devices, and Clusters

IN THIS CHAPTER Executing machine learning algorithms with estimators Defining features and feature columns Using pre-built estimators in practice Analyzing complex datasets with wide and deep learning 

Developing Applications with Estimators

IN THIS CHAPTER Developing projects for the Google Cloud Platform (GCP) Using the GCP utilities Accessing Google Cloud Storage (GCS) Packaging and running TensorFlow applications in the GCP 

Running Applications on the Google Cloud Platform (GCP)

IN THIS CHAPTER Exploring fundamental classes of the TensorFlow API Storing tensors and operations in graphs Using estimators and iterators 

The Ten Most Important Classes

IN THIS CHAPTER Preprocessing data to ensure suitable analysis Selecting the weights and layers of a neural network Choosing an activation function to produce acceptable output 

Community Questions