tensorflow机器学攻略 人工智能 (美)尼克·麦克卢尔(nick mcclure) 新华正版
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作者(美)尼克·麦克卢尔(nick mcclure)
出版社东南大学出版社
ISBN9787564173661
出版时间2017-10
版次1
装帧平装
开本16
页数351页
字数455千字
定价84元
货号xhwx_1201663812
上书时间2024-09-24
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目录：
preface
chapter 1： getting started with tensorflow
introduction
how tensorflow works
declaring tensors
using ceholders and variables
working with matrices
declaring operations
implementing activation functions
working with data sources
additional resources
chapter 2：the tensorflow way
introduction
operations in a putational graph
layering nested operations
working with multiple layers
implementing loss functions
implementing back propagation
working with batch and stochastic training
bining everything together
evaluating models
chapter 3： linear regression
introduction
using the matrix inverse method
implementing a deition method
learning the tensorflow way of linear regression
understan loss functions in linear regression
implementing deming regression
implementing lasso and ridge regression
implementing elastlc regression
implementing logistic regression
chapter 4： support vector machines
introduction
working with a linear svm
reduction to linear regression
working with kernels in tensorflow
implementing a non—linear svm
implementing a multi—class svm
chapter 5： nearest neior methods
introduction
working with nearest neiors
working with text—based distances
puting with mixed distance functions
using an address matching example
using nearest neiors for image recognition
chapter 6： neural works
introduction
implementing operational gates
working with gates and activation functions
implementing a one—layer neural work
implementing different layers
using a multilayer neural work
improving the predictions of linear models
learning to y tic tac toe
chapter 7： natural language processing
introduction
working with bag of words
implementing tf—idf
working with skip—gram embeds
working with cbow embeds
making predictions with word2vec
using doc2vec for sentiment analysis
chapter 8： convolutional neural works
introduction
implementing a simpler n
implementing an advanced n
retraining esting ns models
applying style／neural—style
implementing deepdream
chapter 9： recurrent neural works
introduction
implementing rnn for spam prediction
implementing an lstm model
stacking multiple lstm layers
creatlng sequence—to—sequence models
training a siamese similarity measure
chapter 10： taking tensorflow to production
introduction
implementing unit tests
using multiple executors
parallelizing tensorflow
taking tensorflow to production
productionalizing tensorflow—an example
chapter 11： more with tensorflow
introduction
visualizing graphs in tensorboard
theres more...
working with a geic algorithm
clustering using k—means
solving a system of odes
index
内容简介：
tenorflow内建深度学的扩展支持，任何能够用计算流图形来表达的计算，都可以使用tenorflow。任何基于梯度的机器学算法都能够受益于tenorflow的自动分化（auto—differentiation）。通过灵活的python接，要在tenorflow中表达想法也会很容易。
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