內容大鋼
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·卡里姆著的《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)演算法開發預測模型。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演算法。最後．該部分講述如何開發一個基於機器的因式分解推薦系統。
第三部分介紹高級預測分析的深度學習架構，包括深度神經網路以及高維和序列數據的遞歸神經網路。最終，使用卷積神經網路進行預測建模，用於情緒識別、圖像分類和情感分析。
目錄
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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