Artificial Intelligence for Big Data: Complete guide to automating Big Data solutions using Artificial Intelligence techniques

Description

Build next-generation Artificial Intelligence systems with Java

Key Features

• Implement AI techniques to build smart applications using Deeplearning4j
• Perform big data analytics to derive quality insights using Spark MLlib
• Create self-learning systems using neural networks, NLP, and reinforcement learning

Who This Book Is ForThis book is for you if you are a data scientist, big data professional, or novice who has basic knowledge of big data and wish to get proficiency in Artificial Intelligence techniques for big data. Some competence in mathematics is an added advantage in the field of elementary linear algebra and calculus.

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

In DetailIn 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.

Style and approachAn easy-to-follow, step-by-step guide to help you get to grips with real-world applications of Artificial Intelligence for big data using Java

Table of Contents

1.  Title Page
2. Copyright and Credits
3. Artificial Intelligence for Big Data
4. Packt Upsell
5. Why subscribe?
6. PacktPub.com
7. Contributors
8. About the authors
9. About the reviewers
10. Packt is searching for authors like you
11. Preface
12. Who this book is for
13. What this book covers
14. To get the most out of this book
15. Download the example code files
16. Download the color images
17. Conventions used
18. Get in touch
19. Reviews
20. Big Data and Artificial Intelligence Systems
21. Results pyramid
22. What the human brain does best
23. Sensory input
24. Storage
25. Processing power
26. Low energy consumption
27. What the electronic brain does best
28. Speed information storage
29. Processing by brute force
30. Best of both worlds
31. Big Data
32. Evolution from dumb to intelligent machines
33. Intelligence
34. Types of intelligence
35. Intelligence tasks classification
36. Big data frameworks
37. Batch processing
38. Real-time processing
39. Intelligent applications with Big Data
40. Areas of AI
41. Frequently asked questions
42. Summary
43. Ontology for Big Data
44. Human brain and Ontology
45. Ontology of information science
46. Ontology properties
47. Advantages of Ontologies
48. Components of Ontologies
49. The role Ontology plays in Big Data
50. Ontology alignment
51. Goals of Ontology in big data
52. Challenges with Ontology in Big Data
53. RDF—the universal data format
54. RDF containers
55. RDF classes
56. RDF properties
57. RDF attributes
58. Using OWL, the Web Ontology Language
59. SPARQL query language
60. Generic structure of an SPARQL query
61. Additional SPARQL features
62. Building intelligent machines with Ontologies
63. Ontology learning
64. Ontology learning process
65. Frequently asked questions
66. Summary
67. Learning from Big Data
68. Supervised and unsupervised machine learning
69. The Spark programming model
70. The Spark MLlib library
71. The transformer function
72. The estimator algorithm
73. Pipeline
74. Regression analysis
75. Linear regression
76. Least square method
77. Generalized linear model
78. Logistic regression classification technique
79. Logistic regression with Spark
80. Polynomial regression
81. Stepwise regression
82. Forward selection
83. Backward elimination
84. Ridge regression
85. LASSO regression
86. Data clustering
87. The K-means algorithm
88. K-means implementation with Spark ML
89. Data dimensionality reduction
90. Singular value decomposition
91. Matrix theory and linear algebra overview
92. The important properties of singular value decomposition
93. SVD with Spark ML
94. The principal component analysis method
95. The PCA algorithm using SVD
96. Implementing SVD with Spark ML
97. Content-based recommendation systems
98. Frequently asked questions
99. Summary
100. Neural Network for Big Data
101. Fundamentals of neural networks and artificial neural networks
102. Perceptron and linear models
103. Component notations of the neural network
104. Mathematical representation of the simple perceptron model
105. Activation functions
106. Sigmoid function
107. Tanh function
108. ReLu
109. Nonlinearities model
110. Feed-forward neural networks
111. Gradient descent and backpropagation
112. Gradient descent pseudocode
113. Backpropagation model
114. Overfitting
115. Recurrent neural networks
116. The need for RNNs
117. Structure of an RNN
118. Training an RNN
119. Frequently asked questions
120. Summary
121. Deep Big Data Analytics
122. Deep learning basics and the building blocks
123. Gradient-based learning
124. Backpropagation
125. Non-linearities
126. Dropout
127. Building data preparation pipelines
128. Practical approach to implementing neural net architectures
129. Hyperparameter tuning
130. Learning rate
131. Number of training iterations
132. Number of hidden units
133. Number of epochs
134. Experimenting with hyperparameters with Deeplearning4j
135. Distributed computing
136. Distributed deep learning
137. DL4J and Spark
138. API overview
139. TensorFlow
140. Keras
141. Frequently asked questions
142. Summary
143. Natural Language Processing
144. Natural language processing basics
145. Text preprocessing
146. Removing stop words
147. Stemming
148. Porter stemming
149. Snowball stemming
150. Lancaster stemming
151. Lovins stemming
152. Dawson stemming
153. Lemmatization
154. N-grams
155. Feature extraction
156. One hot encoding
157. TF-IDF
158. CountVectorizer
159. Word2Vec
160. CBOW
161. Skip-Gram model
162. Applying NLP techniques
163. Text classification
164. Introduction to Naive Bayes’ algorithm
165. Random Forest
166. Naive Bayes’ text classification code example
167. Implementing sentiment analysis
168. Frequently asked questions
169. Summary
170. Fuzzy Systems
171. Fuzzy logic fundamentals
172. Fuzzy sets and membership functions
173. Attributes and notations of crisp sets
174. Operations on crisp sets
175. Properties of crisp sets
176. Fuzzification
177. Defuzzification
178. Defuzzification methods
179. Fuzzy inference
180. ANFIS network
181. Adaptive network
182. ANFIS architecture and hybrid learning algorithm
183. Fuzzy C-means clustering
184. NEFCLASS
185. Frequently asked questions
186. Summary
187. Genetic Programming
188. Genetic algorithms structure
189. KEEL framework
190. Encog machine learning framework
191. Encog development environment setup
192. Encog API structure
193. Introduction to the Weka framework
194. Weka Explorer features
195. Preprocess
196. Classify
197. Attribute search with genetic algorithms in Weka
198. Frequently asked questions
199. Summary
200. Swarm Intelligence
201. Swarm intelligence
202. Self-organization
203. Stigmergy
204. Division of labor
205. Advantages of collective intelligent systems
206. Design principles for developing SI systems
207. The particle swarm optimization model
208. PSO implementation considerations
209. Ant colony optimization model
210. MASON Library
211. MASON Layered Architecture
212. Opt4J library
213. Applications in big data analytics
214. Handling dynamical data
215. Multi-objective optimization
216. Frequently asked questions
217. Summary
218. Reinforcement Learning
219. Reinforcement learning algorithms concept
220. Reinforcement learning techniques
221. Markov decision processes
222. Dynamic programming and reinforcement learning
223. Learning in a deterministic environment with policy iteration
224. Q-Learning
225. SARSA learning
226. Deep reinforcement learning
227. Frequently asked questions
228. Summary
229. Cyber Security
230. Big Data for critical infrastructure protection
231. Data collection and analysis
232. Anomaly detection
233. Corrective and preventive actions
234. Conceptual Data Flow
235. Components overview
236. Hadoop Distributed File System
237. NoSQL databases
238. MapReduce
239. Apache Pig
240. Hive
241. Understanding stream processing
242. Stream processing semantics
243. Spark Streaming
244. Kafka
245. Cyber security attack types
246. Phishing
247. Lateral movement
248. Injection attacks
249. AI-based defense
250. Understanding SIEM
251. Visualization attributes and features
252. Splunk
253. Splunk Enterprise Security
254. Splunk Light
255. ArcSight ESM
256. Frequently asked questions
257. Summary
258. Cognitive Computing
259. Cognitive science
260. Cognitive Systems
261. A brief history of Cognitive Systems
262. Goals of Cognitive Systems
263. Cognitive Systems enablers
264. Application in Big Data analytics
265. Cognitive intelligence as a service
266. IBM cognitive toolkit based on Watson
267. Watson-based cognitive apps
268. Developing with Watson
269. Setting up the prerequisites
270. Developing a language translator application in Java
271. Frequently asked questions
272. Summary
273. Other Books You May Enjoy
274. Leave a review – let other readers know what you think

Authors Biography

Anand Deshpande is the Director of big data delivery at Datametica Solutions. He is responsible for partnering with clients on their data strategies and helps them become data-driven. He has extensive experience with big data ecosystem technologies. He has developed a special interest in data science, cognitive intelligence, and an algorithmic approach to data management and analytics. He is a regular speaker on data science and big data at various events.

Manish Kumar is a Senior Technical Architect at Datametica Solutions. He has more than 11 years of industry experience in data management as a data, solutions, and product architect. He has extensive experience in building effective ETL pipelines, implementing security over Hadoop, implementing real-time data analytics solutions, and providing innovative and best possible solutions to data science problems. He is a regular speaker on big data and data science.