Tensorflow预测分析(影印版) (德)礼萨·卡里姆(Rezaul Karim) 著 pdf pdb 阿里云 极速 mobi caj kindle 下载

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。

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

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