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包郵 精通TensorFlow 1.x
開本：
24cm
頁數(shù)：
458頁
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深度學(xué)習(xí)
精通TensorFlow 1.x 版權(quán)信息
- ISBN：9787564182922
- 條形碼：9787564182922 ; 978-7-5641-8292-2
- 裝幀：一般膠版紙
- 冊數(shù)：暫無
- 重量：暫無
- 所屬分類：>
精通TensorFlow 1.x 本書特色
作為一本綜合指南，《精通TensorFlow1.x（影印版 英文版）》將帶領(lǐng)你探究TensorFlow 1.x的高級特性。深入了解TensorFlow Core、Keras、TF Estimators、TFLearn、TF-Slim、Pretty Tensor以及Sonnet。通過TensorFlow和Keras的強(qiáng)大功能，利用轉(zhuǎn)移學(xué)習(xí)、生成式對抗網(wǎng)絡(luò)、深度強(qiáng)化學(xué)習(xí)等概念構(gòu)建深度學(xué)習(xí)模型。在《精通TensorFlow1.x（影印版 英文版）》中，你將獲得各種數(shù)據(jù)集（如MNIST、CIFAR-10、PTB、text8、COCO-Images）的實踐經(jīng)驗。你將學(xué)習(xí)到TensorFlow1.x的高級特性，例如帶有TF-Clusters的分布式TensorFlow、使用TensorFlow Serving部署生產(chǎn)模型、在Android和iOS平臺上為移動和嵌入式設(shè)備構(gòu)建和部署TensorFlow模型。你還會看到如何在R統(tǒng)計軟件中調(diào)用TensorFlow和Keras API，了解在基于TensorFlow API的代碼無法按預(yù)期工作時所需的調(diào)試技術(shù)。 作為一本綜合指南，《精通TensorFlow1.x（影印版 英文版）》將帶領(lǐng)你探究TensorFlow 1.x的高級特性。深入了解TensorFlow Core、Keras、TF Estimators、TFLearn、TF-Slim、Pretty Tensor以及Sonnet。通過TensorFlow和Keras的強(qiáng)大功能，利用轉(zhuǎn)移學(xué)習(xí)、生成式對抗網(wǎng)絡(luò)、深度強(qiáng)化學(xué)習(xí)等概念構(gòu)建深度學(xué)習(xí)模型。在《精通TensorFlow1.x（影印版 英文版）》中，你將獲得各種數(shù)據(jù)集（如MNIST、CIFAR-10、PTB、text8、COCO-Images）的實踐經(jīng)驗。你將學(xué)習(xí)到TensorFlow1.x的高級特性，例如帶有TF-Clusters的分布式TensorFlow、使用TensorFlow Serving部署生產(chǎn)模型、在Android和iOS平臺上為移動和嵌入式設(shè)備構(gòu)建和部署TensorFlow模型。你還會看到如何在R統(tǒng)計軟件中調(diào)用TensorFlow和Keras API，了解在基于TensorFlow API的代碼無法按預(yù)期工作時所需的調(diào)試技術(shù)。 作為一本綜合指南，《精通TensorFlow1.x（影印版 英文版）》將帶領(lǐng)你探究TensorFlow 1.x的高級特性。深入了解TensorFlow Core、Keras、TF Estimators、TFLearn、TF-Slim、Pretty Tensor以及Sonnet。通過TensorFlow和Keras的強(qiáng)大功能，利用轉(zhuǎn)移學(xué)習(xí)、生成式對抗網(wǎng)絡(luò)、深度強(qiáng)化學(xué)習(xí)等概念構(gòu)建深度學(xué)習(xí)模型。在《精通TensorFlow1.x（影印版 英文版）》中，你將獲得各種數(shù)據(jù)集（如MNIST、CIFAR-10、PTB、text8、COCO-Images）的實踐經(jīng)驗。你將學(xué)習(xí)到TensorFlow1.x的高級特性，例如帶有TF-Clusters的分布式TensorFlow、使用TensorFlow Serving部署生產(chǎn)模型、在Android和iOS平臺上為移動和嵌入式設(shè)備構(gòu)建和部署TensorFlow模型。你還會看到如何在R統(tǒng)計軟件中調(diào)用TensorFlow和Keras API，了解在基于TensorFlow API的代碼無法按預(yù)期工作時所需的調(diào)試技術(shù)。
精通TensorFlow 1.x 內(nèi)容簡介
作為一本綜合指南，本書將帶領(lǐng)你探究TensorFlow 1.x的高級特性。深入了解TensorFlow Core、Keras、TF Estimators、TFLearn、TF-Slim、Pretty Tensor以及Sonnet。通過TensorFlow和Keras的強(qiáng)大功能，利用轉(zhuǎn)移學(xué)習(xí)、生成式對抗網(wǎng)絡(luò)、深度強(qiáng)化學(xué)習(xí)等概念構(gòu)建深度學(xué)習(xí)模型。在本書中，你將獲得各種數(shù)據(jù)集(如MNIST、CIFAR-10、PTB、text8、COCO-Images)的實踐經(jīng)驗。你將學(xué)習(xí)到TensorFlow1.x的高級特性，例如帶有TF-Clusters的分布式TensorFlow、使用TensorFlow Serving部署生產(chǎn)模型、在Android和iOS平臺上為移動和嵌入式設(shè)備構(gòu)建和部署TensorFlow模型。你還會看到如何在R統(tǒng)計軟件中調(diào)用TensorFlow和Keras API，了解在基于TensorFlow API的代碼無法按預(yù)期工作時所需的調(diào)試技術(shù)。
精通TensorFlow 1.x 目錄
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
Other Books You May Enjoy
Index
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
Other Books You May Enjoy
Index
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