Deep Learning with DeepLearning4J and Spring Boot
summary
Overview of a concrete Java deployment: a Spring Boot app uses DeepLearning4J with CUDA acceleration to train and serve neural models, visualizes training, exports/imports models, and exposes monitoring and control via Spring Boot Actuator endpoints. Runtime behavior keeps a current model in production while background workers train enhanced models, compares them and hot‑swaps if accuracy improves, and archives replaced models in a pool to build labeled datasets from ensemble agreement. Tools and deployment pattern are demonstrated end‑to‑end.
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chapters (19)
- 0:00Intro
- 4:29Unit Cell
- 5:24Applications
- 6:27Computational Filters
- 8:13Dependence
- 9:53Complexity
- 11:22Optimization
- 11:59Training vs Test
- 13:11Implicit Parameters
- 14:27ImageNet
- 15:30DeepLearning4J
- 22:40Learning for Java
- 24:29Deep Learning for Java
- 28:49Spring Boot Actuator
- 30:16Custom Actuator
- 32:06Strategy
- 36:23Approach
- 36:56Motivations
- 38:51Recommendations
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