精通TensorFlow 1.x(影印版) 东南大学出版社 mobi 下载 网盘 caj lrf pdf txt 阿里云

作为一本综合指南，本书将带领你探究TensorFlow 1.x的不错特性。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的不错特性，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

Neural Network Models in Keras

Workflow for building models in Keras

Creating the Keras model

Sequential API for creating the Keras model

Functional API for creating the Keras model

Keras Layers

Keras core layers

Keras convolutional layers

Keras pooling layers

Keras locally-connected layers

Keras recurrent layers

Keras embedding layers

Keras merge layers

Keras advanced activation layers

Keras normalization layers

Keras noise layers

Adding Layers to the Keras Model

Sequential API to add layers to the Keras model

Functional API to add layers to the Keras Model

Compiling the Keras model

Training the Keras model

Predicting with the Keras model

Additional modules in Keras

Keras sequential model example for MNIST dataset

Summary

Chapter 4: Classical Machine Learning with TensorFIow

Chapter 5: Neural Networks and MLP with TensorFlow and Keras

Chapter 6: RNN with TensorFlow and Keras

Chapter 7: RNN for Time Series Data with TensorFlow and Keras

Chapter 8: RNN for Text Data with TensorFlow and Keras

Chapter 9: CNN with TensorFlow and Keras

Chapter 10: Autoencoder with TensorFlow and Keras

Chapter 11: TensorFlow Models in Production with TF Serving

Chapter 12: Transfer Learning and Pre-Trained Models

Chapter 13: Deep Reinforcement Learning

Chapter 14: Generative Adversarial Networks

Chapter 15: Distributed Models with TensorFlow Clusters

Chapter 16: TensorFlow Models on Mobile and Embedded Platforms

Chapter 17: TensorFlow and Keras in R

Chapter 18: Debuqclincl TensorFlow Models

Appendix: Tensor Processing Units

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