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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

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