TensorFlow預(yù)測(cè)分析（影印版）
定 價(jià)：106 元
- 作者： Rezaul Karim著
- 出版時(shí)間：2018/8/1
- ISBN：9787564177522
- 出 版 社：東南大學(xué)出版社
- 中圖法分類：TP18
- 頁(yè)碼：
- 紙張：膠版紙
- 版次：1
- 開(kāi)本：16K
從結(jié)構(gòu)化和非結(jié)構(gòu)化數(shù)據(jù)中預(yù)測(cè)分析發(fā)現(xiàn)隱藏的 模式，可用于商業(yè)智能決策。
禮薩·卡里姆著的《TensorFlow預(yù)測(cè)分析(影印 版)(英文版)》將通過(guò)在三個(gè)主要部分中運(yùn)用Tensor Flow，幫助你構(gòu)建、調(diào)優(yōu)和部署預(yù)測(cè)模型。**部分 包括預(yù)測(cè)建模所需的線性代數(shù)、統(tǒng)計(jì)學(xué)和概率論知識(shí) 。
第二部分包括運(yùn)用監(jiān)督(分類和回歸)和無(wú)監(jiān)督( 聚類)算法開(kāi)發(fā)預(yù)測(cè)模型。然后介紹如何開(kāi)發(fā)自然語(yǔ) 言處理(NLP)預(yù)測(cè)模型以及強(qiáng)化學(xué)習(xí)算法。*后．該 部分講述如何開(kāi)發(fā)一個(gè)基于機(jī)器的因式分解**系統(tǒng) 。
第三部分介紹**預(yù)測(cè)分析的深度學(xué)習(xí)架構(gòu)，包 括深度神經(jīng)網(wǎng)絡(luò)以及高維和序列數(shù)據(jù)的遞歸神經(jīng)網(wǎng)絡(luò) 。*終，使用卷積神經(jīng)網(wǎng)絡(luò)進(jìn)行預(yù)測(cè)建模，用于情緒 識(shí)別、圖像分類和情感分析。
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
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