更新时间:2021-06-25 21:57:52
封面
版权信息
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Preface
Who this book is for
What this book covers
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Conventions used
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Big Data and Artificial Intelligence Systems
Results pyramid
What the human brain does best
Sensory input
Storage
Processing power
Low energy consumption
What the electronic brain does best
Speed information storage
Processing by brute force
Best of both worlds
Big Data
Evolution from dumb to intelligent machines
Intelligence
Types of intelligence
Intelligence tasks classification
Big data frameworks
Batch processing
Real-time processing
Intelligent applications with Big Data
Areas of AI
Frequently asked questions
Summary
Ontology for Big Data
Human brain and Ontology
Ontology of information science
Ontology properties
Advantages of Ontologies
Components of Ontologies
The role Ontology plays in Big Data
Ontology alignment
Goals of Ontology in big data
Challenges with Ontology in Big Data
RDF—the universal data format
RDF containers
RDF classes
RDF properties
RDF attributes
Using OWL the Web Ontology Language
SPARQL query language
Generic structure of an SPARQL query
Additional SPARQL features
Building intelligent machines with Ontologies
Ontology learning
Ontology learning process
Learning from Big Data
Supervised and unsupervised machine learning
The Spark programming model
The Spark MLlib library
The transformer function
The estimator algorithm
Pipeline
Regression analysis
Linear regression
Least square method
Generalized linear model
Logistic regression classification technique
Logistic regression with Spark
Polynomial regression
Stepwise regression
Forward selection
Backward elimination
Ridge regression
LASSO regression
Data clustering
The K-means algorithm
K-means implementation with Spark ML
Data dimensionality reduction
Singular value decomposition
Matrix theory and linear algebra overview
The important properties of singular value decomposition
SVD with Spark ML
The principal component analysis method
The PCA algorithm using SVD
Implementing SVD with Spark ML
Content-based recommendation systems
Neural Network for Big Data
Fundamentals of neural networks and artificial neural networks