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Artificial Intelligence for Big Data
Thisbookisforyouifyouareadatascientist,bigdataprofessional,ornovicewhohasbasicknowledgeofbigdataandwishtogetproficiencyinArtificialIntelligencetechniquesforbigdata.Somecompetenceinmathematicsisanaddedadvantageinthefieldofelementarylinearalgebraandcalculus.
目錄(273章)
倒序
- 封面
- 版權(quán)信息
- Packt Upsell
- Why subscribe?
- PacktPub.com
- Contributors
- About the authors
- About the reviewers
- Packt is searching for authors like you
- Preface
- Who this book is for
- What this book covers
- To get the most out of this book
- Download the example code files
- Download the color images
- Conventions used
- Get in touch
- Reviews
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- Leave a review - let other readers know what you think 更新時間：2021-06-25 21:57:52
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