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Cognitive IoT Anomaly Detector
with DeepLearning4J on IoT Sensor Data
@romeokienzler
DevDays, Lithuania, 17th May 2017
• 6000+ clients
• $200 million investment
• Partners including
• Avnet, BNP Paribas, EEBus,

Capgemini, Tech Mahindra,

Vodafone, BMW, Visa, Bosch,

Indiegogo, French national

railway SNCF,Arrow Electronics,

Intel, Cisco
Why IoT (now) ?
• 15 Billion connected devices in 2015
• 40 Billion connected devices in 2020
• World population 7.4 Billion in 2016
Machine Learning on
historic data
Source: deeplearning4j.org
Online Learning
Source: deeplearning4j.org
online vs. historic
• Pros
• low storage costs
• real-time model update
• Cons
• algorithm support
• software support
• no algorithmic improvement
• compute power to be inline
with data rate
• Pros
• all algorithms
• abundance of software
• model re-scoring / re-
parameterisation (algorithmic
improvement)
• batch processing
• Cons
• high storage costs
• batch model update
DeepLearning
DeepLearning
Apache Spark
Hadoop
Neural Networks
Neural Networks
Neural Networks
Neural Networks
Neural Networks
Neural Networks
Neural Networks
Neural Networks
Neural Networks
Neural Networks
Neural Networks
Deeper (more) Layers
Convolutional
Convolutional
+ =
Convolutional
Learning of a function
A neural network can basically learn any
mathematical function
Recurrent
http://www.theverge.com/2015/7/17/8985699/stanford-neural-
networks-image-recognition-google-study
http://www.media.uzh.ch/en/Press-Releases/2016/drohnen-
suchen-selbstaendig-auf-waldwegen-nach-vermissten-.html
LSTM
“vanishing gradient problem” == influence of past inputs
decay quickly over time
http://karpathy.github.io/2015/05/21/rnn-effectiveness/
https://m.soundcloud.com/optometrist-prime/recurrence-
music-written-by-a-recurrent-neural-network
•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
Learning of an algorithm
A LSTM network is Turing complete
1
1: http://binds.cs.umass.edu/papers/1995_Siegelmann_Science.pdf
Learning of an algorithm
V2
The Differentiable Neural Computer
Problems
• Neural Networks are computationally very complex
•especially during training
•but also during scoring
CPU (2009) GPU (2016) IBM TrueNorth (2017)
IBM TrueNorth
•Scalable
•Parallel
•Distributed
•Fault Tolerant
•No Clock ! :)
•IBM Cluster
•4.096 chips
•4 billion neurons
•1 trillion synapses
•Human Brain
•100 billion neurons
•100 trillion synapses
•1.000.000 neurons
•250.000.000 synapses
Watson IoT
Watson
Cognitive
Services
Analytics
Model + API Driver Behaviour
Personality
Insights
Trainable Model
+ API
IoT for Insurance
Visual
Recognition
Customizable
Model + API
Watson Machine
Learning
Data Science
Platform as a
Service
Data Science
Experience
DeepLearning4J
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
Cognitive IoT using DeepLearning on data parallel frameworks like Spark & Flink
Cognitive IoT using DeepLearning on data parallel frameworks like Spark & Flink
Cognitive IoT using DeepLearning on data parallel frameworks like Spark & Flink
Cognitive IoT using DeepLearning on data parallel frameworks like Spark & Flink
Cognitive IoT using DeepLearning on data parallel frameworks like Spark & Flink
Cognitive IoT using DeepLearning on data parallel frameworks like Spark & Flink
Cognitive IoT using DeepLearning on data parallel frameworks like Spark & Flink

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