Artificial intelligence for Big Data: complete guide to automating Big Data solutions using artificial intelligence techniques
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Birmingham ; Mumbai
Packt
May 2018
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | vii, 367 Seiten |
| ISBN: | 9781788472173 |
Internformat
MARC
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Datensatz im Suchindex
| _version_ | 1804178464684638208 |
|---|---|
| adam_text | Table of Contents
Preface _________1
Chapter 1: Big Data and Artificial Intelligence Systems g
Results pyramid 10
What the human brain does best 11
Sensory input 11
Storage 11
Processing power 12
Low energy consumption 12
What the electronic brain does best 12
Speed information storage 12
Processing by brute force 13
Best of both worlds 13
Big Data 14
Evolution from dumb to intelligent machines 16
Intelligence 17
Types of intelligence 17
Intelligence tasks classification 18
B ig d ata fra m ewo rks 18
Batch processing 19
Real-time processing 20
Intelligent applications with Big Data 21
Areas of Al 21
Frequently asked questions 21
Summary 23
Chapter 2: Ontology for Big Data 25
Human brain and Ontology 26
Ontology of information science 28
Ontology properties 29
Advantages of Ontologies 30
Components of Ontologies 31
The role Ontology plays in Big Data 32
Ontology alignment 34
Goals of Ontology in big data 34
Challenges with Ontology in Big Data 35
RDF—the universal data format 35
RDF containers 38
RDF classes 39
RDF properties 39
RDF attributes 40
Table of Contents
Using OWL, the Web Ontology Language 40
SPARQL query language 42
Generic structure of an SPARQL query 44
Additional SPARQL features 45
Building intelligent machines with Ontologies 46
Ontology learning 49
Ontology learning process 50
Frequently asked questions 52
Summary 53
Chapter 3: Learning from Big Data 55
Supervised and unsupervised machine learning 56
The Spark programming model 61
The Spark MLlib library 64
The transformer function 64
The estimator algorithm 65
Pipeline 65
Regression analysis 66
Linear regression 67
Least square method 67
Generalized linear model 71
Logistic regression classification technique 71
Logistic regression with Spark 73
Polynomial regression 73
Stepwise regression 75
Forward selection 75
Backward elimination 75
Ridge regression 76
LASSO regression 76
Data clustering 76
The K-means algorithm 78
K-means implementation with Spark ML 80
Data dimensionality reduction 81
Singular value decomposition 83
Matrix theory and linear algebra overview 83
The important properties of singular value decomposition 87
SVD with Spark ML 87
The principal component analysis method 89
The PCA algorithm using SVD 90
Implementing SVD with Spark ML 90
Content-based recommendation systems 91
Frequently asked questions 96
Summary 97
Chapter 4: Neural Network for Big Data 99
Table of Contents
Fundamentals of neural networks and artificial neural networks 100
Perceptron and linear models 102
Component notations of the neural network 103
Mathematical representation of the simple perceptron model 104
Activation functions 106
Sigmoid function 107
Tanh function 108
ReLu 108
Nonlinearities model 110
Feed-forward neural networks 110
Gradient descent and backpropagation 112
Gradient descent pseudocode 116
Backpropagation model 117
Overfitting 119
Recurrent neural networks 121
The need for RNNs 121
Structure of an RNN 122
Training an RNN 122
Frequently asked questions 124
Summary 126
Chapter 5: Deep Big Data Analytics 127
Deep learning basics and the building blocks 128
Gradient-based learning 130
Backpropagation 132
Non-linearities 134
Dropout 136
Building data preparation pipelines 137
Practical approach to implementing neural net architectures 144
Hyperparameter tuning 147
Learning rate 148
Number of training iterations 149
Number of hidden units 150
Number of epochs 150
Experimenting with hyperparameters with Deeplearning4j 151
Distributed computing 156
Distributed deep learning 158
DL4J and Spark 159
API overview 159
TensorFlow 161
Keras 162
Frequently asked questions 163
Summary 165
Chapter 6: Natural Language Processing 167
[iii]
Table of Contents
Natural language processing basics 168
Text preprocessing 170
Removing stop words 170
Stemming 172
Porter stemming 172
Snowball stemming 173
Lancaster stemming 173
Lovins stemming 174
Dawson stemming 174
Lemmatization 175
N-grams 175
Feature extraction 176
One hot encoding 176
TF-IDF 177
CountVectorizer 180
Word2Vec 181
CBOW 181
Skip-Gram model 183
Applying NLP techniques 184
Text classification 185
Introduction to Naive Bayes algorithm 186
Random Forest 187
Naive Bayes text classification code example 188
Implementing sentiment analysis 190
Frequently asked questions 192
Summary 193
Chapter 7: Fuzzy Systems 195
Fuzzy logic fundamentals 196
Fuzzy sets and membership functions 197
Attributes and notations of crisp sets 198
Operations on crisp sets 199
Properties of crisp sets 200
Fuzzification 200
Defuzzification 203
Defuzzification methods 203
Fuzzy inference 203
ANFIS network 204
Adaptive network 204
ANFIS architecture and hybrid learning algorithm 205
Fuzzy C-means clustering 208
NEFCLASS 212
Frequently asked questions 214
Summary 215
Chapter 8: Genetic Programming 217
---------------------------------- [iv] ------------------------------------
Table of Contents
Genetic algorithms structure 220
KEEL framework 223
Encog machine learning framework 228
Encog development environment setup 228
Encog API structure 228
Introduction to the Weka framework 232
Weka Explorer features 237
Preprocess 237
Classify 240
Attribute search with genetic algorithms in Weka 245
Frequently asked questions 248
Summary 248
Chapter 9: Swarm Intelligence 249
Swarm intelligence 250
Self-organization 251
Stigmergy 253
Division of labor 253
Advantages of collective intelligent systems 254
Design principles for developing SI systems 255
The particle swarm optimization model 256
PSO implementation considerations 259
Ant colony optimization model 260
MASON Library 263
MASON Layered Architecture 264
Opt4J library 268
Applications in big data analytics 270
Handling dynamical data 273
Multi-objective optimization 273
Frequently asked questions 274
Summary 275
Chapter 10: Reinforcement Learning 277
Reinforcement learning algorithms concept 278
Reinforcement learning techniques 282
Markov decision processes 282
Dynamic programming and reinforcement learning 284
Learning in a deterministic environment with policy iteration 285
Q-Learning 288
SARSA learning 297
Deep reinforcement learning 299
Frequently asked questions 300
Summary 301
Chapter 11: Cyber Security 303
---------------------------------- [v] -------------------------------------
Table of Contents
Big Data for critical infrastructure protection 304
Data collection and analysis 305
Anomaly detection 306
Corrective and preventive actions 307
Conceptual Data Flow 308
Components overview 309
Hadoop Distributed File System 309
NoSQL databases 310
MapReduce 310
Apache Pig 311
Hive 311
Understanding stream processing 312
Stream processing semantics 313
Spark Streaming 314
Kafka 315
Cyber security attack types 318
Phishing 318
Lateral movement 318
Injection attacks 319
Al-based defense 319
Understanding SIEM 321
Visualization attributes and features 323
Splunk 324
Splunk Enterprise Security 325
Splunk Light 325
ArcSight ESM 328
Frequently asked questions 328
Summary 330
Chapter 12: Cognitive Computing 331
Cognitive science 332
Cognitive Systems 336
A brief history of Cognitive Systems 337
Goals of Cognitive Systems 339
Cognitive Systems enablers 341
Application in Big Data analytics 342
Cognitive intelligence as a service 344
IBM cognitive toolkit based on Watson 345
Watson-based cognitive apps 346
Developing with Watson 349
Setting up the prerequisites 349
Developing a language translator application in Java 351
Frequently asked questions 354
Summary 355
Other Books You May Enjoy___________________________________________________357
Table of Contents
Index
361
|
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| id | DE-604.BV044906095 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-10T08:04:23Z |
| institution | BVB |
| isbn | 9781788472173 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-030299763 |
| oclc_num | 1045037514 |
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| owner | DE-355 DE-BY-UBR DE-945 DE-739 DE-11 |
| owner_facet | DE-355 DE-BY-UBR DE-945 DE-739 DE-11 |
| physical | vii, 367 Seiten |
| publishDate | 2018 |
| publishDateSearch | 2018 |
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| spelling | Deshpande, Anand Verfasser (DE-588)1163487392 aut Artificial intelligence for Big Data complete guide to automating Big Data solutions using artificial intelligence techniques Anand Deshpande, Manish Kumar Birmingham ; Mumbai Packt May 2018 vii, 367 Seiten txt rdacontent n rdamedia nc rdacarrier Big Data (DE-588)4802620-7 gnd rswk-swf Künstliche Intelligenz (DE-588)4033447-8 gnd rswk-swf Künstliche Intelligenz (DE-588)4033447-8 s Big Data (DE-588)4802620-7 s DE-604 Manish Kumar 1976- Verfasser (DE-588)1122136021 aut Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=030299763&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Deshpande, Anand Manish Kumar 1976- Artificial intelligence for Big Data complete guide to automating Big Data solutions using artificial intelligence techniques Big Data (DE-588)4802620-7 gnd Künstliche Intelligenz (DE-588)4033447-8 gnd |
| subject_GND | (DE-588)4802620-7 (DE-588)4033447-8 |
| title | Artificial intelligence for Big Data complete guide to automating Big Data solutions using artificial intelligence techniques |
| title_auth | Artificial intelligence for Big Data complete guide to automating Big Data solutions using artificial intelligence techniques |
| title_exact_search | Artificial intelligence for Big Data complete guide to automating Big Data solutions using artificial intelligence techniques |
| title_full | Artificial intelligence for Big Data complete guide to automating Big Data solutions using artificial intelligence techniques Anand Deshpande, Manish Kumar |
| title_fullStr | Artificial intelligence for Big Data complete guide to automating Big Data solutions using artificial intelligence techniques Anand Deshpande, Manish Kumar |
| title_full_unstemmed | Artificial intelligence for Big Data complete guide to automating Big Data solutions using artificial intelligence techniques Anand Deshpande, Manish Kumar |
| title_short | Artificial intelligence for Big Data |
| title_sort | artificial intelligence for big data complete guide to automating big data solutions using artificial intelligence techniques |
| title_sub | complete guide to automating Big Data solutions using artificial intelligence techniques |
| topic | Big Data (DE-588)4802620-7 gnd Künstliche Intelligenz (DE-588)4033447-8 gnd |
| topic_facet | Big Data Künstliche Intelligenz |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=030299763&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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