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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 (2):

Krishna Choppella

Uday Kamath

View More author details

Table of Contents (5) Chapters

Preface

1. Module 1 FREE CHAPTER

2. Module 2

3. Module 3

Index

Chapter 11. What is Next?

This chapter brings us to the end of our journey of reviewing machine learning Java libraries and discussing how to leverage them to solve real-life problems. However, this should not be the end of your journey by all means. This chapter will give you some practical advice on how to start deploying your models in the real world, what are the catches, and where to go to deepen your knowledge. It also gives you further pointers about where to find additional resources, materials, venues, and technologies to dive deeper into machine learning.

This chapter will cover the following topics:

Important aspects of machine learning in real life
Standards and markup languages
Machine learning in the cloud
Web resources and competitions

Machine learning in real life

Papers, conference presentations, and talks often don't discuss how the models were actually deployed and maintained in production environment. In this section, we'll look into some aspects that should...

The rest of the chapter is locked

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Authors (5)

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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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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Jennifer L. Reese

Jennifer L. Reese

Jennifer L. Reese studied computer science at Tarleton State University. She also earned her M.Ed. from Tarleton in December 2016. She currently teaches computer science to high-school students. Her interests include the integration of computer science concepts with other academic disciplines, increasing diversity in computer science courses, and the application of data science to the field of education. She has co-authored two books: Java for Data Science and Java 7 New Features Cookbook. She previously worked as a software engineer. In her free time she enjoys reading, cooking, and traveling—especially to any destination with a beach. She is a musician and appreciates a variety of musical genres.

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Bostjan Kaluza

Bostjan Kaluza

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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Richard M. Reese

Richard M. Reese

Richard Reese has worked in the industry and academics for the past 29 years. For 10 years he provided software development support at Lockheed and at one point developed a C based network application. He was a contract instructor providing software training to industry for 5 years. Richard is currently an Associate Professor at Tarleton State University in Stephenville Texas. Richard is the author of various books and video courses some of which are as follows: Natural Language Processing with Java. Java for Data Science Getting Started with Natural Language Processing in Java

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