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出版社:東南大學
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ISBN:9787564177522
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作者:(德)禮薩·卡裡姆
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頁數:496
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出版日期:2018-08-01
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印刷日期:2018-08-01
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包裝:平裝
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開本:16開
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版次:1
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印次:1
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字數:636千字
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從結構化和非結構化數據中預測分析發現隱藏的 模式,可用於商業智能決策。 禮薩·卡裡姆著的《TensorFlow預測分析(影印 版)(英文版)》將通過在三個主要部分中運用Tensor Flow,幫助你構建、調優和部署預測模型。第一部分 包括預測建模所需的線性代數、統計學和概率論知識 。 第二部分包括運用監督(分類和回歸)和無監督( 聚類)算法開發預測模型。然後介紹如何開發自然語 言處理(NLP)預測模型以及強化學習算法。最後.該 部分講述如何開發一個基於機器的因式分解推薦繫統 。 第三部分介紹高級預測分析的深度學習架構,包 括深度神經網絡以及高維和序列數據的遞歸神經網絡 。最終,使用卷積神經網絡進行預測建模,用於情緒 識別、圖像分類和情感分析。
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Preface Chapter 1: Basic Python and Linear Algebra for Predictive Analytics A basic introduction to predictive analytics Why predictive analytics? Working principles of a predictive model A bit of linear algebra Programming linear algebra Installing and getting started with Python Installing on Windows Installing Python on Linux Installing and upgrading PIP (or PIP3) Installing Python on Mac OS Installing packages in Python Getting started with Python Python data types Using strings in Python Using lists in Python Using tuples in Python Using dictionary in Python Using sets in Python Functions in Python Classes in Python Vectors, matrices, and graphs Vectors Matrices Matrix addition Matrix subtraction Finding the determinant of a matrix Finding the transpose of a matrix Solving simultaneous linear equations Eigenvalues and eigenvectors Span and linear independence Principal component analysis Singular value decomposition Data compression in a predictive model using SVD Predictive analytics tools in Python Summary Chapter 2: Statistics, Probability, and Information Theory for Predictive Modeling Using statistics in predictive modeling Statistical models Parametric versus nonparametric model Population and sample Random sampling Expectation Central limit theorem Skewness and data distribution Standard deviation and variance Covariance and correlation Interquartile, range, and quartiles Hypothesis testing Chi-square tests Chi-square independence test Basic probability for predictive modeling Probability and the random variables Generating random numbers and setting the seed Probability distributions Marginal probability Conditional probability The chain rule of conditional probability Independence and conditional independence Bayes' rule Using information theory in predictive modeling Self-information Mutual information Entropy Shannon entropy Joint entropy Conditional entropy Information gain Using information theory …… Chapter 3: From Data to Decisions - Getting Started with TensorFlow Chapter 4: Putting Data in Place -Supervised Learning for Predictive Analvtics Chapter 5: Clustering Your Data - Unsupervised Learning for Predictive Analytics Chapter 6: Predictive Analytics Pipelines for NLP Chapter 7: Using Deep Neural Networks for Predictive Analytics Chapter 8: Using Convolutional Neural Networks for Predictive Analvtics Chapter 9: Using Recurrent Neural Networks for Predictive Analytics Chapter 10: Recommendation Systems for Predictive Analytics Chapter 11: Using Reinforcement Learning for Predictive Analytics
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