[Dummies 01] • TensorFlow For Dummies, 1 by Scarpino, Matthew -- Read -- Imperial Library of Trantor

Index

Cover Title Page Table of Contents Introduction

About This Book Foolish Assumptions Icons Used in this Book Beyond the Book Where to Go from Here

Part 1: Getting to Know TensorFlow

Chapter 1: Introducing Machine Learning with TensorFlow

Understanding Machine Learning The Development of Machine Learning Machine Learning Frameworks

Chapter 2: Getting Your Feet Wet

Installing TensorFlow Exploring the TensorFlow Installation Running Your First Application Setting the Style

Chapter 3: Creating Tensors and Operations

Creating Tensors Creating Tensors with Known Values Creating Tensors with Random Values Transforming Tensors Creating Operations Putting Theory into Practice

Chapter 4: Executing Graphs in Sessions

Forming Graphs Creating and Running Sessions Writing Messages to the Log Visualizing Data with TensorBoard Putting Theory into Practice

Chapter 5: 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

Part 2: Implementing Machine Learning

Chapter 6: Analyzing Data with Statistical Regression

Chapter 7: 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

Chapter 8: Classifying Images with Convolutional Neural Networks (CNNs)

Filtering Images Convolutional Neural Networks (CNNs) Putting Theory into Practice Performing Image Operations Putting Theory into Practice

Chapter 9: 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)

Part 3: Simplifying and Accelerating TensorFlow

Chapter 10: Accessing Data with Datasets and Iterators

Datasets Iterators Putting Theory into Practice Bizarro Datasets

Chapter 11: Using Threads, Devices, and Clusters

Executing with Multiple Threads Configuring Devices Executing TensorFlow in a Cluster

Chapter 12: 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

Chapter 13: 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

Part 4: The Part of Tens

Chapter 14: The Ten Most Important Classes

Tensor Operation Graph Session Variable Optimizer Estimator Dataset Iterator Saver

Chapter 15: 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

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