- TensorFlow预测分析(影印版)(英文版)
- - 作者：(德)礼萨·卡里姆
  - 出版社：东南大学
  - ISBN：9787564177522
  - 出版日期：2018/08/01
  - 页数：496
- 售价：42.4

内容大纲
    从结构化和非结构化数据中预测分析发现隐藏的模式，可用于商业智能决策。
    礼萨·卡里姆著的《TensorFlow预测分析(影印版)(英文版)》将通过在三个主要部分中运用Tensor Flow，帮助你构建、调优和部署预测模型。第一部分包括预测建模所需的线性代数、统计学和概率论知识。
    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。最后．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。最终，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。
作者介绍
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

内容大纲

作者介绍

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