New Live Stream: Java-Friendly Machine Learning With the JSR381

Ida Bzowska

Most machine learning (ML) toolkits require knowing Python. Although this is one of the leading programming languages in data science and ML, you can find several convenient alternatives. In the next IntelliJ IDEA Live Stream, we will create an ML model step-by-step using only JSR-381.

Our two guests, Frank Greco and Zoran Sevarac, will show us how to simplify ML in Java. Join us on Wednesday, May 25, at 17:00 CET / 15:00 UTC.

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Session description

Machine learning (ML) is a hugely important global trend that affects every part of the stack from the user to the hardware. All developers, and especially Java developers, need to understand how to build apps that use ML.

However, there are not many coding options for Java developers. The current ML libraries available for Java developers have several issues – either they are very complex and designed for data scientists, or they are Java wrappers around C/C++ libraries and don’t “feel” like Java tools.

Now there is JSR381, an open-source, Java-friendly API for ML, specifically visual recognition. This API has a wide range of business applications across many types of industries and use cases. In this session, we will discuss the goals of JSR381 (“VisRec”), review the API, and show code running in IntelliJ IDEA.

Useful resources

To get the most out of the live stream, you may need additional materials on the topic. Here are the most valuable: Getting Started with VisRec, JSR 381 Reference Implementation, and Deep Netts Community Edition. 

If you have any questions, ask them during our show! All you need to do is join us live and submit your queries via YouTube chat.

Your presenter and host

Zoran Sevarac

Zoran Sevarac is an AI and software engineering professor, entrepreneur, and open-source enthusiast. He is a Java champion and co-author of the JSR 381 API, a Java standard API for Visual Recognition using machine learning. His main professional goal is to strengthen the Java AI / machine learning ecosystem. He is an author and co-founder of the Deep Netts development platform for deep learning in Java, which also provides open-source reference implementation of JSR381.

Frank Greco

Frank Greco has surfed a wide variety of software development waves, from working on telephone call traffic optimization at AT&T Bell Laboratories to co-authoring a visual recognition standard using neural networks and machine learning. He is a Java Champion and the Chairman of the very active and opinionated NYJavaSIG.

Along with Zoran, Frank is the co-author of the JSR 381 Visual Recognition for Java standard API specification and a strong advocate for Java and machine learning. Frank’s goal is to excite, inspire, and educate software developers, technology managers, and organizations on cutting-edge trends and technology.

Mala Gupta

Mala works as a Java Developer Advocate with JetBrains. A Java Champion, she has authored multiple books with Manning, Packt, and O’Reilly Publications. She has 20 years of experience in the software industry and is a frequent speaker at international industry conferences. She actively supports Java certification as a path to career advancement and co-leads Delhi JUG and Women Who Code Delhi.

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