Deep Learning and Java
summary
Java options are practical: Deep Learning for Java (DL4J) wraps a C/C++ core with CUDA support and Hadoop/Spark integration so training and inference run on JVM clusters and GPUs; a Word2Vec demo trains quickly and returns nearest words (e.g., "day" → week, night, game, year). Deep Java Library (DJL) (MXNet backend by default) runs BERT QA in under ~80 lines of Java and answers "December 2004" / "April 2006" from a paragraph. Models serialize for production inference.
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chapters (9)
- 0:00Introduction to deep learning in Java
- 7:12Deep learning vs machine learning
- 16:40History and evolution of neural networks
- 27:06Deep learning libraries for Java
- 33:34Word embeddings and language modeling
- 44:22Demo: word similarity with Java
- 51:02Deep Java Library overview
- 53:10Demo: BERT for question answering
- 1:00:03Closing and Java deep learning options
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