Artificial Intelligence for Big Data Complete guide to automating Big Data solutions using Artificial Intelligence technique 1st edition by Anand Deshpande,Manish Kumar 9781788472173 1788472179

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Product details:

ISBN 10:1788472179
ISBN 13:9781788472173
Author:Anand Deshpande,Manish Kumar

Key Features

• Implement Artificial Intelligence techniques to build smart applications using Deeplearning4j a Java deep learning library
• Create smart self learning and decision making systems
• Develop smarter Big data applications to get better insights from your analysis

Book Description

In the age of big data, companies have growing amount of consumer data than ever before – far more than human and their current technologies can ever hope to keep up with. Artificial Intelligence closes the gap by moving far past human limitations to consume and analyze data.

This book will help you create smart systems to extract intelligent insights for decision making. We will learn about widely used Artificial Intelligence techniques for carrying out solutions in a production ready environment. Explore advanced topics like neural network, clustering, symbolic and sub-symbolic information representation and many more.

Later we will use machine learning algorithms such as K-means, SVM, RBF, regression to perform advance data analysis. We will take you through an understanding of the current status of Machine and Deep Learning techniques to Genetic and Neuro-Fuzzy algorithms. This book aims to address how to develop Artificial Intelligence algorithms to learn from data, why they are necessary, and how they can help to solve real world problems.

By the end of this book, you’ll learn how to implement various Artificial Intelligence algorithms for your big data systems and integrate into your product offerings such as search, image recognition and language processing, Genetic Algorithms, and Fuzzy Logic systems

What you will learn

• Manage Artificial Intelligence techniques for Big Data with Java.
• Build smart systems that can analyze vast and growing data to apply the results for enhanced customer experiences
• Learn to use Artificial Intelligence Frameworks for Big Data.
• Understand and manage complex problems with Genetic Algorithms and Neuro-Fuzzy Systems
• Design an approach to leverage data using the steps in the machine learning process.
• Apply deep learning techniques to explore and prepare data for modeling.
• Construct models that learn from data using widely available open source tools
• Analyze big data problems using scalable machine learning algorithms on Spark.

Artificial Intelligence for Big Data Complete guide to automating Big Data solutions using Artificial Intelligence technique 1st Table of contents:

Who this book is for

What this book covers

To get the most out of this book

Download the example code files

Download the color images

Conventions used

Get in touch

Reviews

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

Frequently asked questions

Summary

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

Frequently asked questions

Summary

Neural Network for Big Data

Fundamentals of neural networks and artificial neural networks

Perceptron and linear models

Component notations of the neural network

Mathematical representation of the simple perceptron model

Activation functions

Sigmoid function

Tanh function

ReLu

Nonlinearities model

Feed-forward neural networks

Gradient descent and backpropagation

Gradient descent pseudocode

Backpropagation model 

Overfitting

Recurrent neural networks

The need for RNNs

Structure of an RNN

Training an RNN

Frequently asked questions

Summary

Deep Big Data Analytics

Deep learning basics and the building blocks

Gradient-based learning

Backpropagation

Non-linearities

Dropout

Building data preparation pipelines

Practical approach to implementing neural net architectures

Hyperparameter tuning

Learning rate

Number of training iterations

Number of hidden units

Number of epochs

Experimenting with hyperparameters with Deeplearning4j

Distributed computing

Distributed deep learning

DL4J and Spark

API overview

TensorFlow

Keras

Frequently asked questions

Summary

Natural Language Processing

Natural language processing basics

Text preprocessing

Removing stop words

Stemming

Porter stemming

Snowball stemming

Lancaster stemming

Lovins stemming

Dawson stemming

Lemmatization

N-grams

Feature extraction

One hot encoding

TF-IDF

CountVectorizer

Word2Vec

CBOW

Skip-Gram model

Applying NLP techniques

Text classification

Introduction to Naive Bayes’ algorithm

Random Forest

Naive Bayes’ text classification code example

Implementing sentiment analysis

Frequently asked questions

Summary

Fuzzy Systems

Fuzzy logic fundamentals

Fuzzy sets and membership functions

Attributes and notations of crisp sets

Operations on crisp sets

Properties of crisp sets

Fuzzification

Defuzzification

Defuzzification methods

Fuzzy inference 

ANFIS network

Adaptive network

ANFIS architecture and hybrid learning algorithm

Fuzzy C-means clustering

NEFCLASS

Frequently asked questions

Summary

Genetic Programming

Genetic algorithms structure

KEEL framework

Encog machine learning framework

Encog development environment setup

Encog API structure

Introduction to the Weka framework

Weka Explorer features

Preprocess

Classify

Attribute search with genetic algorithms in Weka

Frequently asked questions

Summary

Swarm Intelligence

Swarm intelligence 

Self-organization

Stigmergy

Division of labor

Advantages of collective intelligent systems

Design principles for developing SI systems

The particle swarm optimization model

PSO implementation considerations 

Ant colony optimization model

MASON Library

MASON Layered Architecture

Opt4J library

Applications in big data analytics

Handling dynamical data

Multi-objective optimization

Frequently asked questions

Summary

Reinforcement Learning

Reinforcement learning algorithms concept

Reinforcement learning techniques

Markov decision processes

Dynamic programming and reinforcement learning

Learning in a deterministic environment with policy iteration

Q-Learning

SARSA learning

Deep reinforcement learning

Frequently asked questions

Summary

Cyber Security

Big Data for critical infrastructure protection

Data collection and analysis

Anomaly detection 

Corrective and preventive actions 

Conceptual Data Flow

Components overview

Hadoop Distributed File System

NoSQL databases

MapReduce

Apache Pig

Hive

Understanding stream processing

Stream processing semantics

Spark Streaming

Kafka

Cyber security attack types

Phishing

Lateral movement

Injection attacks

AI-based defense 

Understanding SIEM

Visualization attributes and features

Splunk

Splunk Enterprise Security

Splunk Light

ArcSight ESM

Frequently asked questions

Summary

Cognitive Computing

Cognitive science

Cognitive Systems

A brief history of Cognitive Systems

Goals of Cognitive Systems

Cognitive Systems enablers

Application in Big Data analytics

Cognitive intelligence as a service

IBM cognitive toolkit based on Watson

Watson-based cognitive apps

Developing with Watson

Setting up the prerequisites

Developing a language translator application in Java

Frequently asked questions

Summary

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Anand Deshpande,Manish Kumar,Intelligence,Artificial