Kamath / Choppella Mastering Java Machine Learning

A Java developer's guide to implementing machine learning and big data architectures
1. Auflage 2017
ISBN: 978-1-78588-855-7
Verlag: De Gruyter
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 556 Seiten

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

Autoren/Hrsg.

Kamath, Uday

Choppella, Krishna

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Neuronale Netzwerke
• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Programmier- und Skriptsprachen

Inhaltsverzeichnis

Table of Contents - Revisiting Machine Learning Basics

- Practical Approach in Real-World Supervised Learning

- Advanced Topics in Clustering and Anomaly Detection
- Methodology for Real-world Semi-Supervised Learning
- Real-time Stream Machine Learning
- Probabilistic Graph Modelling
- Deep Learning
- Probabilistic Graph Modeling and Graph Data Learning
- Related Topics in Machine Learning
- Linear Algebra
- Probability

Über Autor(innen)

Kamath Uday:

Dr. Uday Kamath is the chief data scientist at BAE Systems Applied Intelligence. He specializes in scalable machine learning and has spent 20 years in the domain of AML, fraud detection in financial crime, cyber security, and bioinformatics, to name a few. Dr. Kamath is responsible for key products in areas focusing on the behavioral, social networking and big data machine learning aspects of analytics at BAE AI. He received his PhD at George Mason University, under the able guidance of Dr. Kenneth De Jong, where his dissertation research focused on machine learning for big data and automated sequence mining.Choppella Krishna:

Krishna Choppella builds tools and client solutions in his role as a solutions architect for analytics at BAE Systems Applied Intelligence. He has been programming in Java for 20 years. His interests are data science, functional programming, and distributed computing.