Jump-Start Your Data Science Learning Java & Jupyter Notebooks
summary
Claims that Java is ready for data science by showing a reproducible Java Jupyter kernel Docker stack with Maven dependency loading and small glue library; runs linear regression on the 150‑row Iris CSV using DF‑lib and Commons Math; generates charts with JFreeChart; downloads models and runs DJL object detection (detects dog, bicycle, truck and draws bounding boxes); and implements a working RAG pipeline with Spring AI and Redis vector DB that stores embeddings and retrieves beer catalog passages for prompt augmentation.
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chapters (11)
- 0:00Java for data science
- 1:49Why Java for ML
- 7:28Jupyter notebooks matter
- 11:42Java stack with containers
- 15:40Jupyter Lab setup
- 21:15Hello world in Jupyter
- 26:15Linear regression in Java
- 33:58Object detection with DJL
- 36:06Visualizing embeddings
- 36:50RAG with Spring AI
- 44:20Closing and future
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