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DeepLearning and Advanced Machine Learning on IoT

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DeepLearning and Advanced Machine Learning on IoT

  1. 1. Unless stated otherwise all images are taken from wikipedia.org or openclipart.org DeepLearning and Advanced Machine Learning on IoT @romeokienzler
  2. 2. • Intro (what I do, what we do, why we do IoT) • Latest advancements in AI based ML • Demo • Hands on
  3. 3. Why IoT (now) ? • 15 Billion connected devices in 2015 • 40 Billion connected devices in 2020 • World population 7.4 Billion in 2016
  4. 4. Machine Learning on historic data Source: deeplearning4j.org
  5. 5. Online Learning Source: deeplearning4j.org
  6. 6. 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
  7. 7. 1. API 2. pre-trained model 3. existing pipeline with your data 4. create own pipeline / model abstraction levels
  8. 8. IBM Watson Personality Insights
  9. 9. IBM Watson Natural Language Classifier
  10. 10. DeepLearning DeepLearning Apache Spark Hadoop
  11. 11. Neural Networks
  12. 12. Neural Networks
  13. 13. Deeper (more) Layers
  14. 14. AutoEncoders
  15. 15. AutoEncoders
  16. 16. Convolutional
  17. 17. Convolutional + =
  18. 18. Convolutional
  19. 19. Learning of a function A neural network can basically learn any mathematical function
  20. 20. Recurrent
  21. 21. LSTM “vanishing error problem” == influence of past inputs decay quickly over time
  22. 22. http://karpathy.github.io/2015/05/21/rnn-effectiveness/
  23. 23. •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
  24. 24. Learning of an algorithm A LSTM network is touring complete
  25. 25. Problems • Neural Networks are computationally very complex •especially during training •but also during scoring CPU (2009) GPU (2016) IBM TrueNorth (2017)
  26. 26. 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
  27. 27. DeepLearning the future in cloud based analytics Storage Layer (OpenStack SWIFT / Hadoop HDFS / IBM GPFS) Execution Layer (Spark Executor, YARN, Platform Symphony) Hardware Layer (Bare Metal High Performance Cluster) GraphXStreaming SQL MLLib BlinkDB DeepLearning4J
 
 ND4J R MLBase H2O Y O U GPUAVX Intel Xeon E7-4850 v2 48 core, 3 TB RAM, 72 GB HDD, 10Gbps, NVIDIA TESLA M60 GPU (cu)BLAS jcuBLAS S T R E A M S
  28. 28. bit.ly/toa16
  29. 29. •IBM Cloud Free Tier •http://ibm.biz/joinIBMCloud •Google TPU •http://www.recode.net/2016/5/20/11719392/google-ai-ch tpu-questions-answers •IBM Neuromorphic Chip •http://www.research.ibm.com/articles/brain-chip.shtm •Recoding of the Talk •https://www.youtube.com/watch?v=h5_NH3sL0Qw •Contact Romeo Kienzler on Twitter: @romeokienzler

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