消息首页搜索举报

TensorFlow预测分析

26.99 2.5折 106 九五品

仅1件

北京通州

认证卖家担保交易快速发货售后保障

作者Rezaul,Karim 著

出版社东南大学出版社

ISBN9787564177522

出版时间2018-08

版次1

装帧平装

开本16开

纸张胶版纸

页数496页

字数99999千字

定价106元

上书时间2024-12-03

纵列風

已实名已认证进店收藏店铺

在售商品暂无
平均发货时间 27小时
好评率暂无

最新上架

2008电脑软装备 ¥18.57

PC宝贝2008：windows XP操作就这500招 ¥14.75

2008数码大家庭 ¥21.00

大数据分析与应用基础 ¥35.46

解码智能时代 ¥19.38

解码智能时代 ¥17.50

融合多源异构数据的推荐与检索 ¥55.47

C语言程序设计与实践 ¥36.61

数据库基础 ¥29.22

商品详情

品相描述：九五品

商品描述: 基本信息
书名:TensorFlow预测分析
定价：106.00元
作者:Rezaul,Karim 著
出版社：东南大学出版社
出版日期：2018-08-01
ISBN：9787564177522
字数：636000
页码：496
版次：1
装帧：平装
开本：16开
商品重量：
编辑推荐

内容提要
《TensorFlow预测分析（）》将通过在三个主要部分中运用TensorFlow，帮助你构建、调优和部署预测模型。部分包括预测建模所需的线性代数、统计学和概率论。第二部分包括运用监督和无监督算法开发预测模型，强化学习算法等。第三部分介绍高级预测分析的深度学习架构，包括深度神经网络以及高维和序列数据的递归神经网络。终，使用卷积神经网络进行预测建模，用于情绪识别、图像分类和情感分析。
目录
PrefaceChapter 1: Basic Pythoand Linear Algebra forPredictive AnalyticsA basic introductioto predictive analyticsWhy predictive analytics?Working principles of a predictive modelA bit of linear algebraProgramming linear algebraInstalling and getting started with PythoInstalling oWindowsInstalling PythooLinuxInstalling and upgrading PIP (or PIP3)Installing PythooMac OSInstalling packages iPythoGetting started with PythoPythodata typesUsing strings iPythoUsing lists iPythoUsing tuples iPythoUsing dictionary iPythoUsing sets iPythoFunctions iPythoClasses iPythoVectors, matrices, and graphsVectorsMatricesMatrix additioMatrix subtractioFinding the determinant of a matrixFinding the transpose of a matrixSolving simultaneous linear equationsEigenvalues and eigenvectorsSpaand linear independencePrincipal component analysisSingular value decompositioData compressioia predictive model using SVDPredictive analytics tools iPythoSummaryChapter 2: Statistics, Probability, and InformatioTheory forPredictive ModelingUsing statistics ipredictive modelingStatistical modelsParametric versus nonparametric modelPopulatioand sampleRandom samplingExpectatioCentral limit theoremSkewness and data distributioStandard deviatioand varianceCovariance and correlatioInterquartile, range, and quartilesHypothesis testingChi-square testsChi-square independence testBasic probability for predictive modelingProbability and the random variablesGenerating random numbers and setting the seedProbability distributionsMarginal probabilityConditional probabilityThe chairule of conditional probabilityIndependence and conditional independenceBayes' ruleUsing informatiotheory ipredictive modelingSelf-informatioMutual informatioEntropyShannoentropyJoint entropyConditional entropyInformatiogaiUsing informatiotheory……Chapter 3: From Data to Decisions - Getting Started with TensorFlowChapter 4: Putting Data iPlace -Supervised Learning for Predictive AnalvticsChapter 5: Clustering Your Data - Unsupervised Learning for Predictive AnalyticsChapter 6: Predictive Analytics Pipelines for NLPChapter 7: Using Deep Neural Networks for Predictive AnalyticsChapter 8: Using Convolutional Neural Networks for Predictive AnalvticsChapter 9: Using Recurrent Neural Networks for Predictive AnalyticsChapter 10: RecommendatioSystems for Predictive AnalyticsChapter 11： Using Reinforcement Learning for Predictive Analytics......
作者介绍

序言
PrefaceChapter 1: Basic Pythoand Linear Algebra forPredictive AnalyticsA basic introductioto predictive analyticsWhy predictive analytics?Working principles of a predictive modelA bit of linear algebraProgramming linear algebraInstalling and getting started with PythonInstalling oWindowsInstalling PythooLinuxInstalling and upgrading PIP (or PIP3)Installing PythooMac OSInstalling packages iPythonGetting started with PythonPythodata typesUsing strings iPythonUsing lists iPythonUsing tuples iPythonUsing dictionary iPythonUsing sets iPythonFunctions iPythonClasses iPythonVectors, matrices, and graphsVectorsMatricesMatrix additionMatrix subtractionFinding the determinant of a matrixFinding the transpose of a matrixSolving simultaneous linear equationsEigenvalues and eigenvectorsSpaand linear independencePrincipal component analysisSingular value decompositionData compressioia predictive model using SVDPredictive analytics tools iPythonSummaryChapter 2: Statistics, Probability, and InformatioTheory forPredictive ModelingUsing statistics ipredictive modelingStatistical modelsParametric versus nonparametric modelPopulatioand sampleRandom samplingExpectationCentral limit theoremSkewness and data distributionStandard deviatioand varianceCovariance and correlationInterquartile, range, and quartilesHypothesis testingChi-square testsChi-square independence testBasic probability for predictive modelingProbability and the random variablesGenerating random numbers and setting the seedProbability distributionsMarginal probabilityConditional probabilityThe chairule of conditional probabilityIndependence and conditional independenceBayes' ruleUsing informatiotheory ipredictive modelingSelf-informationMutual informationEntropyShannoentropyJoint entropyConditional entropyInformatiogainUsing informatiotheory……Chapter 3: From Data to Decisions - Getting Started with TensorFlowChapter 4: Putting Data iPlace -Supervised Learning for Predictive AnalvticsChapter 5: Clustering Your Data - Unsupervised Learning for Predictive AnalyticsChapter 6: Predictive Analytics Pipelines for NLPChapter 7: Using Deep Neural Networks for Predictive AnalyticsChapter 8: Using Convolutional Neural Networks for Predictive AnalvticsChapter 9: Using Recurrent Neural Networks for Predictive AnalyticsChapter 10: RecommendatioSystems for Predictive AnalyticsChapter 11： Using Reinforcement Learning for Predictive Analytics......

【封面】

— 没有更多了 —

TensorFlow预测分析

纵列風

商品详情

相关推荐