精通TensorFlow1.x(影印版)(英文版) pdf 下载 txt 阿里云 lit rtf azw3 免费

作为一本综合指南，本书将带领你探究TensorFlow 1.x的 特性。深入了解TensorFlow Core、Keras、TF Estimators、TFLearn、TF-Slim、Pretty Tensor以及Sonnet。通过TensorFlow和Keras的强大功能，利用转移学习、生成式对抗网络、深度强化学习等概念构建深度学习模型。在本书中，你将获得各种数据集（如MNIST、CIFAR-10、PTB、text8、COCO-Images）的实践经验。你将学习到TensorFlow1.x的 特性，例如带有TF-Clusters的分布式TensorFlow、使用TensorFlow Serving部署生产模型、在Android和iOS平台上为移动和嵌入式设备构建和部署TensorFlow模型。你还会看到如何在R统计软件中调用TensorFlow和Keras API，了解在基于TensorFlow API的代码无法按预期工作时所需的调试技术。

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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