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Hands-on artificial intelligence with Java for beginners : build intelligent apps using machine learning and deep learning with Deeplearning4j /
This book will introduce the AI algorithms to the beginners and will take on implementing AI tasks using various Java-based libraries. It will take a practical approach to get you up and running with building smarter applications using Java programming knowledge.
| Clasificación: | Libro Electrónico |
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| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham, UK :
Packt Publishing,
2018.
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| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
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 SystemIntroducing 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 attributesAttribute 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 modelsSummary; Other Books You May Enjoy; Index