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TensorFlow For Dummies
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Preface
About This Book
Foolish Assumptions
Icons Used in this Book
Beyond the Book
Where to Go from Here
About the Author
Dedication
Author’s Acknowledgments
1
Introducing Machine Learning with TensorFlow
Understanding Machine Learning
The Development of Machine Learning
Machine Learning Frameworks
Getting Your Feet Wet
Installing TensorFlow
Exploring the TensorFlow Installation
Running Your First Application
Setting the Style
Creating Tensors and Operations
Creating Tensors
Creating Tensors with Known Values
Creating Tensors with Random Values
Transforming Tensors
Creating Operations
Putting Theory into Practice
Executing Graphs in Sessions
Forming Graphs
Creating and Running Sessions
Writing Messages to the Log
Visualizing Data with TensorBoard
Putting Theory into Practice
Training
Training in TensorFlow
Formulating the Model
Looking at Variables
Determining Loss
Minimizing Loss with Optimization
Feeding Data into a Session
Monitoring Steps, Global Steps, and Epochs
Saving and Restoring Variables
Working with SavedModels
Putting Theory into Practice
Visualizing the Training Process
Session Hooks
Analyzing Data with Statistical Regression
Analyzing Systems Using Regression
Linear Regression: Fitting Lines to Data
Polynomial Regression: Fitting Polynomials to Data
Binary Logistic Regression: Classifying Data into Two Categories
Multinomial Logistic Regression: Classifying Data into Multiple Categories
Introducing Neural Networks and Deep Learning
From Neurons to Perceptrons
Improving the Model
Layers and Deep Learning
Training with Backpropagation
Implementing Deep Learning
Tuning the Neural Network
Managing Variables with Scope
Improving the Deep Learning Process
Classifying Images with Convolutional Neural Networks (CNNs)
Filtering Images
Convolutional Neural Networks (CNNs)
Putting Theory into Practice
Performing Image Operations
Putting Theory into Practice
Analyzing Sequential Data with Recurrent Neural Networks (RNNs)
Recurrent Neural Networks (RNNs)
Creating RNN Cells
Long Short-Term Memory (LSTM) Cells
Gated Recurrent Units (GRUs)
Accessing Data with Datasets and Iterators
Datasets
Iterators
Putting Theory into Practice
Bizarro Datasets
Using Threads, Devices, and Clusters
Executing with Multiple Threads
Configuring Devices
Executing TensorFlow in a Cluster
Developing Applications with Estimators
Introducing Estimators
Training an Estimator
Testing an Estimator
Running an Estimator
Creating Input Functions
Using Feature Columns
Creating and Using Estimators
Running Estimators in a Cluster
Accessing Experiments
Running Applications on the Google Cloud Platform (GCP)
Overview
Working with GCP Projects
The Cloud Software Development Kit (SDK)
The gcloud Utility
Google Cloud Storage
Preparing for Deployment
Executing Applications with the Cloud SDK
Configuring a Cluster in the Cloud
The Ten Most Important Classes
Tensor
Operation
Graph
Session
Variable
Optimizer
Estimator
Dataset
Iterator
Saver
Ten Recommendations for Training Neural Networks
Select a Representative Dataset
Standardize Your Data
Use Proper Weight Initialization
Start with a Small Number of Layers
Add Dropout Layers
Train with Small, Random Batches
Normalize Batch Data
Try Different Optimization Algorithms
Set the Right Learning Rate
Check Weights and Gradients
Index
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