Artificial intelligence for big data :: complete guide to automating big data solutions using artificial intelligence techniques.
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 re...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Weitere Verfasser: | |
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Birmingham :
Packt Publishing,
2018.
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | 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. |
| Beschreibung: | 1 online resource (371 pages) |
| ISBN: | 9781788476010 1788476018 1788472179 9781788472173 |
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| 505 | 0 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
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| 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. 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. 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. 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. |
| ctrlnum | (OCoLC)1038486328 |
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| dewey-ones | 005 - Computer programming, programs, data, security |
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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.</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">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.</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">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 -- 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You will learn about widely used Artificial Intelligence techniques for carrying out solutions in a production-ready environment. 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| genre | Electronic book. |
| genre_facet | Electronic book. |
| id | ZDB-4-EBA-on1038486328 |
| illustrated | Not Illustrated |
| indexdate | 2024-11-27T13:28:58Z |
| institution | BVB |
| isbn | 9781788476010 1788476018 1788472179 9781788472173 |
| language | English |
| oclc_num | 1038486328 |
| open_access_boolean | |
| owner | MAIN DE-863 DE-BY-FWS |
| owner_facet | MAIN DE-863 DE-BY-FWS |
| physical | 1 online resource (371 pages) |
| psigel | ZDB-4-EBA |
| publishDate | 2018 |
| publishDateSearch | 2018 |
| publishDateSort | 2018 |
| publisher | Packt Publishing, |
| record_format | marc |
| spelling | Deshpande, Anand. Artificial intelligence for big data : complete guide to automating big data solutions using artificial intelligence techniques. Birmingham : Packt Publishing, 2018. 1 online resource (371 pages) text txt rdacontent computer c rdamedia online resource cr rdacarrier Online resource; title from PDF title page (EBSCO, viewed September 11, 2018). 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. 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. 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. 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. 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. Big data. http://id.loc.gov/authorities/subjects/sh2012003227 Business logistics Data processing. Données volumineuses. Logistique (Organisation) Informatique. COMPUTERS Databases Data Warehousing. bisacsh Big data fast Business logistics Data processing fast Electronic book. Kumar, Manish. FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1817513 Volltext |
| spellingShingle | Deshpande, Anand Artificial intelligence for big data : complete guide to automating big data solutions using artificial intelligence techniques. 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. 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. 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. 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. Big data. http://id.loc.gov/authorities/subjects/sh2012003227 Business logistics Data processing. Données volumineuses. Logistique (Organisation) Informatique. COMPUTERS Databases Data Warehousing. bisacsh Big data fast Business logistics Data processing fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh2012003227 |
| 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. |
| title_fullStr | Artificial intelligence for big data : complete guide to automating big data solutions using artificial intelligence techniques. |
| title_full_unstemmed | Artificial intelligence for big data : complete guide to automating big data solutions using artificial intelligence techniques. |
| 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. http://id.loc.gov/authorities/subjects/sh2012003227 Business logistics Data processing. Données volumineuses. Logistique (Organisation) Informatique. COMPUTERS Databases Data Warehousing. bisacsh Big data fast Business logistics Data processing fast |
| topic_facet | Big data. Business logistics Data processing. Données volumineuses. Logistique (Organisation) Informatique. COMPUTERS Databases Data Warehousing. Big data Business logistics Data processing Electronic book. |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1817513 |
| work_keys_str_mv | AT deshpandeanand artificialintelligenceforbigdatacompleteguidetoautomatingbigdatasolutionsusingartificialintelligencetechniques AT kumarmanish artificialintelligenceforbigdatacompleteguidetoautomatingbigdatasolutionsusingartificialintelligencetechniques |