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Machine Learning: End-to-End guide for Java developers

You're reading from Machine Learning: End-to-End guide for Java developers Data Analysis, Machine Learning, and Neural Networks simplified

Product type Course

Published in Oct 2017

Publisher Packt

ISBN-13 9781788622219

Length 1159 pages

Edition 1st Edition

Languages

Java

Tools

Deeplearning4j

Concepts

Data Analysis

Authors (5):

Boštjan Kaluža

Jennifer L. Reese

Krishna Choppella

Richard M. Reese

Uday Kamath

+1 more

View More author details

Table of Contents (5) Chapters

Preface

1. Module 1 FREE CHAPTER

2. Module 2

3. Module 3

Index

Part 2. Module 2

Machine Learning in Java
Design, build, and deploy your own machine learning applications by leveraging key Java machine learning libraries

The rest of the chapter is locked

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83

Tech Concepts

36

Programming languages

73

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Authors (5)

Boštjan Kaluža

Boštjan Kaluža

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.

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Jennifer L. Reese

Jennifer L. Reese

Jennifer L. Reese holds a B.S. degree from Tarleton State University. She currently works as a software engineer for Local Government Solutions in Waxahachie, Texas, developing software for county government. Prior to graduation, she worked for the Center for Agribusiness Excellence at Tarleton where she used Java in conjunction with GIS software to analyze crop and weather data.

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Krishna Choppella

Krishna Choppella

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.

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Richard M. Reese

Richard M. Reese

Richard M. Reese holds a Ph.D. in Computer Science from Texas A & M University and is currently an Associate Professor in the Department of Engineering and Physics at Tarleton State University in Stephenville, Texas. In addition to his experience in academia, Richard has over 17 years experience in industry including operating system development at GTE Automatic Electric Labs and at Lockheed Martin in Fort Worth, Texas, where he supervised a tool development group and oversaw various research and development projects. Prior to his industrial experience he served four years in the United States Air Force. Richard has been involved with Java since 1996 and is a certified Java SE 7 Associate Programmer. He has worked as consultant/instructor of software languages in private and public classes providing him with a variety of insight into industry applications. He has published numerous papers and has developed courseware for a variety of topics including advanced Java technologies. He has also written the EJB 3.1 Cookbook and Java 7 New Features Cookbook for Packt Publishing.

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Uday Kamath

Uday Kamath

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.

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