Book Image

Artificial Intelligence for Big Data

By : Anand Deshpande, Manish Kumar
Book Image

Artificial Intelligence for Big Data

By: Anand Deshpande, Manish Kumar

Overview of this book

In this age of big data, companies have larger amount of consumer data than ever before, far more than what the current technologies can ever hope to keep up with. However, Artificial Intelligence closes the gap by moving past human limitations in order to analyze data. With the help of Artificial Intelligence for big data, you will learn to use Machine Learning algorithms such as k-means, SVM, RBF, and regression to perform advanced data analysis. You will understand the current status of Machine and Deep Learning techniques to work on Genetic and Neuro-Fuzzy algorithms. In addition, you will explore how to develop Artificial Intelligence algorithms to learn from data, why they are necessary, and how they can help solve real-world problems. By the end of this book, you'll have learned how to implement various Artificial Intelligence algorithms for your big data systems and integrate them into your product offerings such as reinforcement learning, natural language processing, image recognition, genetic algorithms, and fuzzy logic systems.

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Table of Contents (19 chapters)
Title Page
Title Page
Copyright and Credits
Copyright and Credits
Packt Upsell
Packt Upsell
Contributors
Contributors
Preface
Preface
Free Chapter
1
Big Data and Artificial Intelligence Systems
Big Data and Artificial Intelligence Systems
Results pyramid
What the human brain does best
What the electronic brain does best
Best of both worlds
Summary
2
Ontology for Big Data
Ontology for Big Data
Human brain and Ontology
Ontology of information science
Summary
3
Learning from Big Data
Learning from Big Data
Supervised and unsupervised machine learning
The Spark programming model
The Spark MLlib library
Regression analysis
Data clustering
The K-means algorithm
Data dimensionality reduction
Singular value decomposition
The principal component analysis method
Content-based recommendation systems
Frequently asked questions
Summary
4
Neural Network for Big Data
Neural Network for Big Data
Fundamentals of neural networks and artificial neural networks
Perceptron and linear models
Nonlinearities model
Feed-forward neural networks
Gradient descent and backpropagation
Overfitting
Recurrent neural networks
Frequently asked questions
Summary
5
Deep Big Data Analytics
Deep Big Data Analytics
Deep learning basics and the building blocks
Building data preparation pipelines
Practical approach to implementing neural net architectures
Hyperparameter tuning
Distributed computing
Distributed deep learning
Frequently asked questions
Summary
6
Natural Language Processing
Natural Language Processing
Natural language processing basics
Text preprocessing
Feature extraction
Applying NLP techniques
Implementing sentiment analysis
Frequently asked questions
Summary
7
Fuzzy Systems
Fuzzy Systems
Fuzzy logic fundamentals
ANFIS network
Fuzzy C-means clustering
NEFCLASS
Frequently asked questions
Summary
8
Genetic Programming
Genetic Programming
Genetic algorithms structure
KEEL framework
Encog machine learning framework
Introduction to the Weka framework
Attribute search with genetic algorithms in Weka
Frequently asked questions
Summary
9
Swarm Intelligence
Swarm Intelligence
Swarm intelligence 
The particle swarm optimization model
Ant colony optimization model
MASON Library
Opt4J library
Applications in big data analytics
Handling dynamical data
Multi-objective optimization
Frequently asked questions
Summary
10
Reinforcement Learning
Reinforcement Learning
Reinforcement learning algorithms concept
Reinforcement learning techniques
Deep reinforcement learning
Frequently asked questions
Summary
11
Cyber Security
Cyber Security
Big Data for critical infrastructure protection
Understanding stream processing
Cyber security attack types
Understanding SIEM
Splunk
ArcSight ESM
Frequently asked questions
Summary
12
Cognitive Computing
Cognitive Computing
Cognitive science
Cognitive Systems
Application in Big Data analytics
Cognitive intelligence as a service
Frequently asked questions
Summary
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Index
Index

Singular value decomposition

As we have seen in the previous section, reducing the dimensions of the datasets increases the efficiency of the model generation, without sacrificing the amount of knowledge contained in the data. As a result, the data is compressed and easy to visualize in fewer dimensions. SVD is a fundamental mathematical tool that can be easily leveraged for dimensionality reduction.

Matrix theory and linear algebra overview

Before we try to understand SVD, here is a quick overview of linear algebra and matrix theory concepts. Although a comprehensive discussion on these topics is outside the scope of this book, a brief discussion is definitely in order:

  • Scalar: A single number is termed a scalar. A scalar represents the magnitude of an entity. For example, the speed of a car is 60 miles/hour. Here, the number 60 is a scalar. 
  • Vectors: An array of multiple scalars arranged in an order is called a vector. Typically, vectors define magnitude as well as direction, and are considered...
Previous Section
End of Section 9
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