Book Description
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.
What You Will Learn
- Manage Artificial Intelligence techniques for big data with Java
- Build smart systems to analyze data for enhanced customer experience
- Learn to use Artificial Intelligence frameworks for big data
- Understand complex problems with algorithms and Neuro-Fuzzy systems
- Design stratagems to leverage data using Machine Learning process
- Apply Deep Learning techniques to prepare data for modeling
- Construct models that learn from data using open source tools
- Analyze big data problems using scalable Machine Learning algorithms
Table of Contents
1: 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
- Human brain and Ontology
- Ontology of information science
- Summary
3: 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
- 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 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 basics
- Text preprocessing
- Feature extraction
- Applying NLP techniques
- Implementing sentiment analysis
- Frequently asked questions
- Summary
7: Fuzzy Systems
- Fuzzy logic fundamentals
- ANFIS network
- Fuzzy C-means clustering
- NEFCLASS
- Frequently asked questions
- Summary
8: 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
- 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 algorithms concept
- Reinforcement learning techniques
- Deep reinforcement learning
- Frequently asked questions
- Summary
11: 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 science
- Cognitive Systems
- Application in Big Data analytics
- Cognitive intelligence as a service
- Frequently asked questions
- Summary
