상세정보

CONTENTS
Preface = vii
1. Introduction = 1
 Going Deep = 1
  Using TensorFlow for AI Systems = 2
 TensorFlow : What''''s in a Name? = 5
 A High-Level Overview = 6
 Summary = 8
2. Go with the Flow : Up and Running with TensorFlow = 9
 Installing TensorFlow = 9
 Hello World = 11
 MNIST = 13
 Softmax Regression = 14
 Summary = 21
3. Understanding TensorFlow Basics = 23
 Computation Graphs = 23
  What Is a Computation Graph? = 23
  The Benefits of Graph Computations = 24
 Graphs, Sessions, and Fetches = 24
  Creating a Graph = 25
  Creating a Session and Running It = 26
  Constructing and Managing Our Graph = 27
  Fetches = 29
 Flowing Tensors = 30
  Nodes Are Operations, Edges Are Tensor Objects = 30
  Data Types = 32
  Tensor Arrays and Shapes = 33
  Names = 37
 Variables, Placeholders, and Simple Optimization = 38
  Variables = 38
  Placeholders = 39
  Optimization = 40
 Summary = 49
4. Convolutional Neural Networks = 51
 Introduction to CNNs = 51
 MNIST : TakeⅡ = 53
  Convolution = 54
  Pooling = 56
  Dropout = 57
  The Model = 57
 CIFAR10 = 61
  Loading the CIFAR10 Dataset = 62
  Simple CIFAR10 Models = 64
 Summary = 68
5. TextⅠ : Working with Text and Sequences, and TensorBoard Visualization = 69
 The Importance of Sequence Data = 69
 Introduction to Recurrent Neural Networks = 70
  Vanilla RNN Implementation = 72
  TensorFlow Built-in RNN Functions = 82
 RNN for Text Sequences = 84
  Text Sequences = 84
  Supervised Word Embeddings = 88
  LSTM and Using Sequence Length = 89
  Training Embeddings and the LSTM Classifier = 91
 Summary = 93
6. TextⅡ : Word Vectors, Advanced RNN, and Embedding Visualization = 95
 Introduction to Word Embeddings = 95
 Word2vec = 97
  Skip-Grams = 98
  Embeddings in TensorFlow = 100
  The Noise-Contrastive Estimation(NCE) Loss Function = 101
  Learning Rate Decay = 101
  Training and Visualizing with TensorBoard = 102
  Checking Out Our Embeddings = 103
  Pretrained Embeddings, Advanced RNN = 105
  Pretrained Word Embeddings = 106
  Bidirectional RNN and GRU Cells = 110
 Summary = 112
7. TensorFlow Abstractions and Simplifications = 113
 Chapter Overview = 113
  High-Level Survey = 115
 contrib.learn = 117
  Linear Regression = 118
  DNN Classifier = 120
  FeatureColumn = 123
  Homemade CNN with contrib.learn = 128
 TFLearn = 131
  Installation = 131
  CNN = 131
  RNN = 134
  Keras = 136
  Pretrained models with TF-Slim = 143
 Summary = 151
8. Queues, Threads, and Reading Data = 153
 The Input Pipeline = 153
 TFRecords = 154
  Writing with TFRecordWriter = 155
 Queues = 157
  Enqueuing and Dequeuing = 157
  Multithreading = 159
  Coordinator and QueueRunner = 160
 A Full Multithreaded Input Pipeline = 162
  tf.train.string_input_producer() and tf.TFRecordReader() = 164
  tf.train.shuffle_batch() = 164
  tf.train.start_queue_runners() and Wrapping Up = 165
 Summary = 166
9. Distributed TensorFlow = 167
 Distributed Computing = 167
  Where Does the Parallelization Take Place? = 168
  What Is the Goal of Parallelization? = 168
 TensorFlow Elements = 169
  tf.app.flags = 169
  Clusters and Servers = 170
  Replicating a Computational Graph Across Devices = 171
  Managed Sessions = 171
  Device Placement = 172
 Distributed Example = 173
 Summary = 179
10. Exporting and Serving Models with TensorFlow = 181
 Saving and Exporting Our Model = 181
  Assigning Loaded Weights = 182
  The Saver Class = 185
 Introduction to TensorFlow Serving = 191
  Overview = 192
  Installation = 193
  Building and Exporting = 194
 Summary = 201
A. Tips on Model Construction and Using TensorFlow Serving = 203
Index = 221