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  • 音像TENSORFLOW深度学习(第2版 )GiancarloZaccone,RezaulKarim
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    • 作者: GiancarloZaccone,RezaulKarim著 | GiancarloZaccone,RezaulKarim编 | GiancarloZaccone,RezaulKarim译 | GiancarloZaccone,RezaulKarim绘
    • 出版社: 东南大学出版社
    • 出版时间:2019-05-01
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    • 作者: GiancarloZaccone,RezaulKarim著| GiancarloZaccone,RezaulKarim编| GiancarloZaccone,RezaulKarim译| GiancarloZaccone,RezaulKarim绘
    • 出版社:东南大学出版社
    • 出版时间:2019-05-01
    • 版次:1
    • 印次:1
    • 字数:592千字
    • 页数:458
    • ISBN:9787564183264
    • 版权提供:东南大学出版社
    • 作者:GiancarloZaccone,RezaulKarim
    • 著:GiancarloZaccone,RezaulKarim
    • 装帧:平装
    • 印次:1
    • 定价:108.00
    • ISBN:9787564183264
    • 出版社:东南大学出版社
    • 开本:暂无
    • 印刷时间:暂无
    • 语种:暂无
    • 出版时间:2019-05-01
    • 页数:458
    • 外部编号:1201912633
    • 版次:1
    • 成品尺寸:暂无

    Preface
    Chapter 1: Getting Started with Deep Learning
    A soft introduction to machine learning
    Supervised learning
    Unbalanced data
    Unsupervised learning
    Reinforcement learning
    What is deep learning?
    Artifi neural networks
    The biological neurons
    The artifi neuron
    How does an ANN learn?
    ANNs and the backpropagation algorithm
    Weight optimization
    Stochastic gradient descent
    Neural network architectures
    Deep Neural Networks (DNNs)
    Multilayer perceptron
    Deep Belief Networks (DBNs)
    Convolutional Neural Networks (CNNs)
    AutoEncoders
    Recurrent Neural Networks (RNNs)
    Emergent architectures
    Deep learning frameworks
    Summary
    Chapter 2: A First Look at TensorFlow
    A general overview of TensorFlow
    Whats new in TensorFlow vl.6?
    Nvidia GPU support optimized
    Introducing TensorFlow Lite
    Eager execution
    Optimized Accelerated Linear Algebra (XLA)
    Installing and configuring TensorFlow
    TensorFlow computational graph
    TensorFlow code structure
    Eager execution with TensorFIow
    Data model in TensorFlow
    Tensor
    Rank and shape
    Da ye
    Variables
    Fetches
    Feeds and placeholders
    Visualizing computations through TensorBoard
    How does TensorBoard work?
    Linear regression and beyond
    Linear regression revisited for a real dataset
    Summary
    Chapter 3: Feed-Forward Neural Networks with TensorFIow
    Feed-forward neural networks (FFNNs)
    Feed-forward and backpropagation
    Weights and biases
    Activation functions
    Using sigmoid
    Using tanh
    Using ReLU
    Using softmax
    Implementing a feed-forward neural network
    Exploring the MNIST dataset
    Softmax classifier
    Implementing a multilayer perceptron (MLP)
    Training an MLP
    Using MLPs
    Dataset description
    Preprocessing
    A TensorFIow implementation of MLP for client-subscription assessment
    Chapter 4: Convolutional Neural Networks
    Chapter 5: Optimizing TensorFIow Autoencoders
    Chapter 6: Recurrent Neural Networks
    Chapter 7: Heterogeneous and Distributed Computing
    Chapter 8: Advanced TensorFIow Programming
    Chapter 9: Recommendation Systems Using Factorization Machines
    Chapter 10: Reinforcement Learning
    Other Books You May Enjoy
    Index

    吉安卡洛?扎克尼(Giancarlo Zaccone),在并行计算和可视化方向拥有丰富经验,目前于某咨询公司担任系统和软件。

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