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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