消息首页搜索举报

Python深度学习算法实践(影印版)(英文版)

全新正版 _可开发票_极速发货

72.62 6.1折 119 全新

库存5件

天津西青

认证卖家担保交易快速发货售后保障

作者(印)苏达桑·拉维尚迪兰

出版社东南大学出版社

ISBN9787564189693

出版时间2020-08

装帧平装

开本16开

定价119元

货号1202113359

上书时间2024-10-05

果然是好书店

三年老店

已实名已认证进店收藏店铺

在售商品暂无
平均发货时间 14小时
好评率暂无

最新上架

植物生理学第3版 ¥44.18

课程与教学论（第二版） ¥48.45

音乐教育心理学 ¥40.85

国际航空运输服务贸易政策研究 ¥45.94

2020年国家火炬特色产业基地发展报告 ¥38.59

统编初中历史教科书教学设计与指导·世界历史·九年级下册 ¥42.58

高中英语教科书教学设计与指导必修第三册（外研版适用） ¥49.50

高中英语教科书教学设计与指导必修第三册 ¥49.50

高中英语教科书教学设计与指导必修第一册（外研版适用） ¥49.50

商品详情

品相描述：全新

商品描述: 目录
Preface
Section 1" Getting Started with Deep Learning
Chapter 1: Introduction to Deep Learning
  What is deep learning?
  Biological and artifi neurons
  ANN and its layers
    Input layer
    Hidden layer
    Output layer
  Exploring activation functions
    The sigmoid function
    The tanh function
    The Rectified Linear Unit function
    The leaky ReLU function
    The Exponential linear unit function
    The Swish function
    The softmax function
  Forward propagation in ANN
  How does ANN learn?
  Debugging gradient descent with gradient checking
  Putting it all together
    Building a neural network from scratch
  Summary
  Questions
  Further reading
Chapter 2: Getting to Know TensorFIow
  What is TensorFIow?
   Understanding computational graphs and sessions
    Sessions
  Variables, constants, and placeholders
    Variables
    Constants
    Placeholders and feed dictionaries
  Introducing TensorBoard
    Creating a name scope
  Handwritten digit classification using TensorFIow
    Importing the required libraries
    Loading the dataset
    Defining the number of neurons in each layer
    Defining placeholders
    Forward propagation
    Computing loss and backpropagation
    Computing accuracy
    Creating summary
    Training the model
    Visualizing graphs in TensorBoard
  Introducing eager execution
  Math operations in TensorFIow
  TensorFIow 2.0 and Keras
    Bonjour Keras
     Defining the model
      Defining a sequential model
      Defining a functional model
     Compiling the model
     Training the model
     Evaluating the model
    MNIST digit classification using TensorFIow 2.0
  Should we use Keras or TensorFIow?
  Summary
  Questions
  Further reading
Section 2: Fundamental Deep Learning Algorithms
Chapter 3: Gradient Descent and Its Variants
  Demystifying gradient descent
    Performing gradient descent in regression "
     Importing the libraries
     Preparing the dataset
     Defining the loss function
     Computing the gradients of the loss function
     Updating the model parameters
  Gradient descent versus stochastic gradient descent
  Momentum-based gradient descent
    Gradient descent with momentum
    Nesterov accelerated gradient
  Adaptive methods of gradient descent
    Setting a learning rate adaptively using Adagrad
    Doing away with the learning rate using Adadelta
    Overcoming the limitations of Adagrad using RMSProp
    Adaptive moment estimation
     Adamax - Adam based on infinity-norm
    Adaptive moment estimation with AMSGrad
……
Section 3 Advanced Deep Learning Algorithms

内容摘要
    本书深入浅出地剖析了深度学习的原理和相关技术。书中使用Python，从基本的数学知识出发，带领读者从零创建一个经典的深度学习网络，使读者在此过程中逐步理解深度学习。书中不仅介绍了深度学习和神经网络的概念、特征等基础知识，对误差反向传播法、卷积神经网络等也有深入讲解，此外还介绍了深度学习相关的实用技巧，自动驾驶、图像生成、强化学习等方面的应用，以及为什么加深层可以提高识别精度等疑难的问题。

— 没有更多了 —