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  • 精通TensorFlow 1.x(影印版) (美)阿曼达·范丹戈(Armando Fandango) 著 专业科技
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    • 作者: (美)阿曼达·范丹戈(Armando Fandango)著
    • 出版社: 东南大学出版社
    • 出版时间:2019-03-01 00:00:00
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    • 作者: (美)阿曼达·范丹戈(Armando Fandango)著
    • 出版社:东南大学出版社
    • 出版时间:2019-03-01 00:00:00
    • 版次:1
    • 印次:1
    • 印刷时间:2019-03-01
    • 字数:583千字
    • 页数:458
    • 开本:16开
    • 装帧:平装
    • ISBN:9787564182922
    • 国别/地区:中国
    • 版权提供:东南大学出版社

    精通TensorFlow 1.x(影印版)

    作  者:(美)阿曼达·范丹戈(Armando Fandango) 著
    定  价:108
    出 版 社:东南大学出版社
    出版日期:2019年03月01日
    页  数:458
    装  帧:平装
    ISBN:9787564182922
    主编推荐

    内容简介

    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Tensnull

    作者简介

    精彩内容

    目录
    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

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