Hands-on artificial intelligence with Java for beginners : build intelligent apps using machine learning and deep learning with deeplearning4j

Hands-on artificial intelligence with Java for beginners build intelligent apps using machine learning and deep learning with deeplearning4j

Build, train, and deploy intelligent applications using Java libraries Key Features Leverage the power of Java libraries to build smart applications Build and train deep learning models for implementing artificial intelligence Learn various algorithms to automate complex tasks Book Description Artif...

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Detalles Bibliográficos
Otros Autores: Joshi, Nisheeth, author (author)
Formato: Libro electrónico
Idioma:Inglés
Publicado: Birmingham ; Mumbai : Packt 2018.
Edición:1st edition
Materias:
JavaScript (Computer program language)
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009630701706719
Tabla de Contenidos:
  • Cover
  • Title Page
  • Copyright and Credits
  • Packt Upsell
  • Contributors
  • Table of Contents
  • Preface
  • Chapter 1: Introduction to Artificial Intelligence and Java
  • What is machine learning?
  • Differences between classification and regression
  • Installing JDK and JRE
  • Setting up the NetBeans IDE
  • Importing Java libraries and exporting code in projects as a JAR file
  • Summary
  • Chapter 2: Exploring Search Algorithms
  • An introduction to searching
  • Implementing Dijkstra's search
  • Understanding the notion of heuristics
  • A brief introduction to the A* algorithm
  • Implementing an A* algorithm
  • Summary
  • Chapter 3: AI Games and the Rule-Based System
  • Introducing the min-max algorithm
  • Implementing an example min-max algorithm
  • Installing Prolog
  • An introduction to rule-based systems with Prolog
  • Setting up Prolog with Java
  • Executing Prolog queries using Java
  • Summary
  • Chapter 4: Interfacing with Weka
  • An introduction to Weka
  • Installing and interfacing with Weka
  • Calling the Weka environment into Java
  • Reading and writing datasets
  • Converting datasets
  • Converting an ARFF file to a CSV file
  • Converting a CSV file to an ARFF file
  • Summary
  • Chapter 5: Handling Attributes
  • Filtering attributes
  • Discretizing attributes
  • Attribute selection
  • Summary
  • Chapter 6: Supervised Learning
  • Developing a classifier
  • Model evaluation
  • Making predictions
  • Loading and saving models
  • Summary
  • Chapter 7: Semi-Supervised and Unsupervised Learning
  • Working with k-means clustering
  • Evaluating a clustering model
  • An introduction to semi-supervised learning
  • The difference between unsupervised and semi-supervised learning
  • Self-training and co-training machine learning models
  • Downloading a semi-supervised package
  • Creating a classifier for semi-supervised models.
  • Making predictions with semi-supervised machine learning models
  • Summary
  • Other Books You May Enjoy
  • Index.

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