目录 Preface Chapter 1: TensorFlow 101 What is TensorFIow? TensorFlow core Code warm-up - Hello TensorFIow Tensors Constants Operations Placeholders Creating tensors from Python objects Variables Tensors generated from library functions Populating tensor elements with the same values Populating tensor elements with sequences Populating tensor elements with a random distribution Getting Variables with tf.get_variable() Data flow graph or computation graph Order of execution and lazy loading Executing graphs across compute devices - CPU and GPGPU Placing graph nodes on specific compute devices Simple placement Dynamic placement Soft placement GPU memory handling Multiple graphs TensorBoard A TensorBoard minimal example TensorBoard details Summary Chapter 2: High-Level Libraries for TensorFlow TF Estimator - previously TF Learn TF Slim TFLearn Creating the TFLearn Layers TFLearn core layers TFLearn convolutional layers TFLearn recurrent layers TFLearn normalization layers TFLearn embedding layers TFLearn merge layers TFLearn estimator layers Creating the TFLearn Model Types of TFLearn models Training the TFLearn Model Using the TFLearn Model PrettyTensor Sonnet Summary Chapter 3: Keras 101 Installing Keras
以下为对购买帮助不大的评价