ML in Java - YES It's Possible

Overview

Unlock the potential of machine learning in Java with our comprehensive guide. This session delves into powerful Java-based frameworks such as Deeplearning4J, DJL, and Tribuo, offering you insights into how you can effectively leverage these tools to build, save, and deploy machine learning models.

Through real-world examples and meticulous comparisons, you'll gain a profound understanding of the capabilities and advantages of using Java for machine learning tasks. Join us to bridge the gap between Java and machine learning and discover how you can implement robust, efficient ML solutions within your Java applications.

Syllabus

• Introduction to Machine Learning in Java
Overview of Java's role in machine learning
Advantages and challenges of using Java for ML
• Java-Based Machine Learning Frameworks
Deeplearning4J
Introduction and setup
Core features and capabilities
Example project: Building a neural network
Deep Java Library (DJL)
Introduction and setup
Core features and capabilities
Example project: Image classification
Tribuo
Introduction and setup
Core features and capabilities
Example project: Regression model
• Building ML Models in Java
Data preprocessing with Java
Defining and training models
Evaluating model performance
• Saving and Deploying Java ML Models
Model serialization and deserialization
Deployment strategies for Java ML models
• Real-World Examples and Comparisons
Case studies of Java ML applications
Comparing Java ML frameworks: performance and use cases
• Practical Session: Developing a Complete Java ML Application
Integrating multiple frameworks
End-to-end application development
• Conclusion and Future Trends
The future of Java in machine learning
Emerging trends and technologies
• Additional Resources and Further Learning
Recommended books, online courses, and documentation
Community resources and support forums

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Subjects

Conference Talks