| Description |
1 online resource (371 pages) |
| Contents |
Cover -- Copyright and Credits -- Packt Upsell -- Contributors -- Table of Contents -- Preface -- Chapter 1: 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 -- Chapter 2: 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 -- Chapter 3: 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. |
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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 -- Chapter 4: 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 -- Chapter 5: 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 -- Chapter 6: Natural Language Processing -- Natural language processing basics -- Text preprocessing -- Removing stop words -- Stemming -- Porter stemming. |
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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 -- Chapter 7: 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 -- Chapter 8: 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 -- Chapter 9: 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 -- Chapter 10: Reinforcement Learning -- Reinforcement learning algorithms concept. |
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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 -- Chapter 11: 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 -- Chapter 12: 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 -- Other Books You May Enjoy -- Index. |
| Summary |
Create smart systems to extract intelligent insights for decision making. You will learn about widely used Artificial Intelligence techniques for carrying out solutions in a production-ready environment. You'll explore advanced topics such as clustering, symbolic and sub-symbolic information representation, and many more. |
| Local Note |
eBooks on EBSCOhost EBSCO eBook Subscription Academic Collection - North America |
| Subject |
Big data.
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Business logistics -- Data processing.
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COMPUTERS -- Databases -- Data Warehousing. |
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Big data |
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Business logistics -- Data processing |
| Added Author |
Kumar, Manish.
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| ISBN |
9781788476010 (electronic bk.) |
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1788476018 (electronic bk.) |
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1788472179 (Trade Paper) |
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9781788472173 |
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9781788472173 |
| Standard No. |
9781788472173 |
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