Sessions at Oracle Code One 2018 where I’m speaking

I’m speaking at two sessions at Oracle Code One 2018 in San Francisco. One about basics of machine learning, and the other about using deep learning for application perfromance tuning. Oracle Code One (ex. Java One) is the biggest and most influential conference for Java technologies in the world.
You can also find me at Oracle Developer Champions briefing on Friday 10.19. at Oracle and Java Champions Briefing on Saturday 10.20. in Parc 55, and Java Influencers Gathering at at Google Developers Launchpad Space on Thursday.  More details are bellow:

Java and Machine Learning  at Java Champions Briefing 2018

Overview of the current Java machine learning landscape, what needs to be done in order to make Java no 1 platform for machine learning development, and ongoing efforts in that domain.

2.00 p.m. - 2.30 p.m. | Parc 55 Hotel San Francisco, Market Room

Deep Learning for Application Performance Optimization

Application performance tuning usually involves periodically monitoring and adjusting several parameters that control the runtime environment, including the CPU, memory, threading, garbage collection, and more. This session presents the experience of the speaker and his team in building deep learning models for autonomous, continuous application performance tuning. The presentation includes a methodology, architecture, and best practices for building such systems.
The participants will learn how to build deep learning models for modeling application performance for various configuration settings. A case study is based on tuning a Java enterprise application but can be generalized for other types of applications or individual components.

Monday, Oct 22, 7:30 p.m. - 8:15 p.m. | Moscone West - Room 2005

If you’re comming to Oracle Code One schedule this session at: https://oracle.rainfocus.com/widget/oracle/oow18/catalogcodeone18?search=BOF4967

Machine Learning for Software Developers in 45 Minutes

Technologists know that machine learning (ML) is a humongous, long-term trend for all applications. Many companies are now applying an “AI first” philosophy in all their applications and services. This session is designed for Java developers who are new to ML and need an overview of ML and where/how to apply it.
You’ll learn about its relationship to AI and why ML is evolving at such a rapid pace.  The session also describes the types of learning styles—supervised learning, unsupervised learning, and reinforcement learning—and also discusses “deep learning.”
It covers use cases and the various ML services offerings from Oracle Cloud, AWS, Azure, and GCP. Most importantly, the focus is on how Java developers can take advantage of ML in their applications.

Monday, Oct 22, 1:30 p.m. - 2:15 p.m. | Moscone West - Room 2016

If you’re comming to Oracle Code One schedule this session at: https://oracle.rainfocus.com/widget/oracle/oow18/catalogcodeone18?search=DEV5090

Sessions at Java One 2017 where I’m speaking

Deep Learning and Image Recognition for Java Developers [BOF2830]

Deep learning provides state-of-the-art image recognition that can be used for tasks such as object recognition, automatic image classification, and tagging. Application domains include augmented reality, visual search, medical imaging, advertising, and more. This session presents a tool for building image recognition solutions based on deep learning and the NetBeans platform. The tool can also be integrated with the NetBeans IDE to provide an out-of-the box deep learning solution for Java developers. The session is intended for developers interested in the development of Java applications that require advanced image recognition. Participants will get the basics of image recognition with deep learning and how to implement it in their Java applications.

Machine Learning for Java Developers in 45 Minutes [CON2977]

This session clarifies and defines machine learning for Java developers. You’ll learn about its relationship to artificial intelligence (AI) and why machine learning is evolving at such a rapid pace. The session also describes the types of learning styles: supervised learning, unsupervised learning, and deep learning. You’ll hear about various use cases that are directly suitable for machine learning. Most importantly, the presentation focuses on how Java developers can take advantage of machine learning in their applications. Together with Frank Greco, Chairman, NYJavaSIG

Machine Learning for Java Developers Training

Machine learning gives computers the ability to learn to solve specific type of tasks without being explicitly programmed.

Machine Learning for Java Developers is a 2 day intensive training designed for Java development teams who want to add machine learning into their solutions. The training gives basic understanding of machine learning concepts and techniques,  and provides hands on exercises in Java code. An exercise during the training also includes a use case of your choice for applying machine learning, preferable related to some of the projects that you’re working on.

You will learn to:

  • Recognize problems and features that could benefit from machine learning
  • Design machine learning procedure for specific problems including data processing, model building and evaluation
  • Use various Java machine learning libraries and tools

Training Content Outline

  1. Introduction to machine learning
  2. Linear Regression
  3. Logistic regression
  4. Classification
  5. Neural networks
    1. Adalines and Perceptrons
    2. Multi Layer Perceptron and Backpropagatopn
  6. Crossvalidation

Prerequisites

  • Skills: Basic Java  programming
  • Software: Java 8, some Java IDE (preferable NetBeans), Maven, Git

Sessions at Java One 2016 where I’m speaking

  • Zoran Sevarac, Assistant Professor, University of Belgrade
    Deep learning is a state-of-the art machine learning technique that has been successfully applied for various image processing applications such as object recognition in images, automatic image tagging, and categorization. This session presents a deep-learning-based Java solution for object recognition in images, suitable for bringing this technique to mobile and IoT devices. The session is intended for developers interested in development of IoT Java applications that require intelligent image processing and of image and video processing software in general. Participants will learn about solutions for deep learning in Java and how to use them in their applications to make them “see” and understand pictures and videos.

  • Zoran Sevarac, Assistant Professor, University of Belgrade
    Johannes Weigend, CTO QAware GmbH, QAware GmbH
  • Zoran Sevarac, Assistant Professor, University of Belgrade
    Johannes Weigend, CTO QAware GmbH, QAware GmbH
    Paul Anderson, President, Anderson Software Group, Inc.
    Kenneth Fogel, Instructor, Concordia University
    Gail Anderson, Research Director, Anderson Software Group, Inc.
  • Zoran Sevarac, Assistant Professor, University of Belgrade
    Anton Epple, Consultant, Dukehoff GmbH
    Mark stephens, IDRsolutions
    Constantin Drabo, Engineer, Universite de Ouagadougou

More at https://oracle.rainfocus.com/scripts/catalog/oow16.jsp?event=javaone&search=%22Zoran%20Sevarac%22

 

Session at JavaOne 2015

Our JavaOne session ‘Stop the Rot: A Discussion on Maintaining Java SoftwareQuality’ has been accepted!

Abstract
Ever heard about software rot? Technical debt? You can avoid these issues by monitoring software quality to develop sustainable and successful software projects. This panel discussion deals with the essential software quality factors and real-world best practices for maintaining software quality in Java. The panel focuses on how to use open source software quality tools for Java to improve design and coding practices. It also demos using software quality tools such as SonarQube, FindBugs, PMD, and SQE in several open source projects and discusses the results. The attendees will learn about available software quality tools and their features and how to use them to produce better software.
Speakers
Zoran Sevarac, Assistant Professor, Faculty of Organisational Sciences, University of Belgrade
Vladan Devedzic, FON – School of Business Administration, University of Belgrade
Sven Reimers, System Engineer, Airbus Defence and Space
Florian Vogler, System Engineer, Airbus Defence and Space
Martijn Verburg, jClarity