The document introduces large-scale deep learning using Deeplearning4j and Apache Spark, highlighting its high-level programming components and capabilities. It discusses tools for data handling and model training, emphasizing advantages like GPU support and parallelization. Additionally, it showcases a predictive maintenance use case demonstrating improved performance over traditional methods.

1. Introduction to Large-Scale
DeepLearning with
DeepLearning4J and
ApacheSpark
@romeokienzler
Swiss Data Science Conference 16 - ZHAW - Winterthur

2. –Assembler vs. Python?
“High-level programming”

3. Components
• DeepLearning4J
Enterprise Grade DeepLearning Library
• DataVec
CSV/Audio/Video/Image/… => Vector
• ND4J / ND4S (NumPy for the JVM)

4. ND4J
• Tensor support (Linear Buffer + Stride)
• Multiple implementations, one interface
• vectorized c++ code (JavaCPP), off-heap data
storage, BLAS (OpenBLAS, Intel MKL, cuBLAS)
• GPU (CUDA 7.5)

5. turn on GPU

6. DL4J parallelisation
• TensorFlow on ApacheSpark =>
• Scoring
• Multi-model hyper-parameter tuning
• Parallel training since V r0.8
• DeepLearning4J =>
• Scoring, Multi-model hyper-parameter tuning
• Parallel training
“Jeff Dean style parameter averaging”

7. “Code local vs spark”
vs.

8. Demo
IoT / Industry / Predictive Maintenance Use Case

13. data
https://github.com/romeokienzler/pmqsimulator
https://ibm.biz/joinIBMCloud

20. •Outperformed traditional
methods, such as
•cumulative sum (CUSUM)
•exponentially weighted moving
average (EWMA)
•Hidden Markov Models (HMM)
•Learned what “Normal” is
•Raised error if time series pattern
haven't been seen before