Zoran previously on airhacks.fm: "#169 Deep Learning with Modern Java Code", discussion about the latest updates and features in DeepNetts, a full-stack Java AI platform, University of Minnesota's drug testing application using DeepNetts, Jefferson Lab's particle research using DeepNetts Community Edition, including GPU support for faster inference using jcuda, TensorFlow compatibility, and simplified AI integration with JSR-381, real-world applications of DeepNetts in drug testing and particle research, challenges and considerations for using GPUs in serverless environments, the potential of Apple's M-series chips for machine learning, exploring Project Babylon and Code Reflection in Java, using Panama and jextract for native library bindings, the importance of having developer tools and an IDE for building AI models, plans for integrating large language models into DeepNetts, the advantages of a pure Java solution for AI in enterprise applications, and the bright future of Java in the AI ecosystem, Deep Nets 3.1.0 release with GPU support

Dr. Zoran Sevarac on twitter: @zsevarac