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Intro to DeepLearning4J on ApacheSpark SDS DL Workshop 16

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Romeo Kienzler
Romeo Kienzler

Swiss Data Science Conference 16 - DeepLearning Workshop https://www.zhaw.ch/en/research/inter-school-cooperation/datalab-the-zhaw-data-science-laboratory/sds2016/sds-deep-learning-day-2016/

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Introduction to Large-Scale DeepLearning with DeepLearning4J and ApacheSpark @romeokienzler Swiss Data Science Conference 16 - ZHAW - Winterthur
–Assembler vs. Python? “High-level programming”
Components • DeepLearning4J
 Enterprise Grade DeepLearning Library • DataVec
 CSV/Audio/Video/Image/… => Vector • ND4J / ND4S (NumPy for the JVM)
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)
turn on GPU
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”
“Code local vs spark” vs.
Demo IoT / Industry / Predictive Maintenance Use Case
data https://github.com/romeokienzler/pmqsimulator https://ibm.biz/joinIBMCloud
•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
Intro to DeepLearning4J on ApacheSpark SDS DL Workshop 16
Intro to DeepLearning4J on ApacheSpark SDS DL Workshop 16

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