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【内容】
本书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。靠前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（分类和回归）和无监督（聚类）算法开发预测模型。然后介绍如何开发自然语言处理（NLP）预测模型，以及强化学习算法。很后，该部分讲述如何开发一个基于机器的因式分解推荐系统。第三部分介绍不错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。很终，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】
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
Index
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
Index
