Bhatia / Kaluza Machine Learning in Java

Helpful techniques to design, build, and deploy powerful machine learning applications in Java
2. Auflage 2018
ISBN: 978-1-78847-389-7
Verlag: De Gruyter
Format: EPUB
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 300 Seiten

No detailed description available for "Machine Learning in Java".

Autoren/Hrsg.

Bhatia, Ashish

Bhatia, Ashishsingh

Kaluza, Bostjan

Fachgebiete

• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Datenbankdesign & Datenbanktheorie
• Mathematik | Informatik EDV | Informatik Informatik Mensch-Maschine-Interaktion Informationsarchitektur
• Mathematik | Informatik EDV | Informatik Informatik Theoretische Informatik
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz

Inhaltsverzeichnis

Table of Contents - Applied Machine Learning Quick Start

- Java Libraries and Platforms for Machine Learning

- Basic Algorithms

- Customer Relationship Prediction with Ensembles
- Affinity Analysis

- Recommendation Engine with Apache Mahout

- Fraud and Anomaly Detection

- Image Recognition with Deeplearning4j

- Activity Recognition with Mobile Phone Sensors
- Text Mining with Mallet

- What is Next?

Über Autor(innen)

Kaluza Bostjan:

Bostjan Kaluza is a researcher in artificial intelligence and machine learning with extensive experience in Java and Python. Bostjan is the chief data scientist at Evolven, a leading IT operations analytics company. He works with machine learning, predictive analytics, pattern mining, and anomaly detection to turn data into relevant information. Prior to Evolven, Bostjan served as a senior researcher in the department of intelligent systems at the Jozef Stefan Institute and led research projects involving pattern and anomaly detection, ubiquitous computing, and multi-agent systems. In 2013, Bostjan published his first book, Instant Weka How-To, published by Packt Publishing, exploring how to leverage machine learning using Weka.