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An Introduction to Artificial Neural Networks

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Do you want to predict customer behavior?  Evaluate the content of a photo or sound? Detect Fraud? Feed usage data back into your algorithms to improve them automatically? All of these things are being done today using Neural Networks for Machine Learning. 

This talk will cover the technologies used to create Neural Networks and give an introduction to the basics of why they work, the different types, and how they are being applied to today's business problems. The topics covered include:

• Artificial Neural Networks
• Convolutional Neural Networks
• Self Organizing Maps
• Recurrent Neural Networks
• AutoEncoders

You'll leave with an understanding of Neural Network terminology and basic concepts, and understand how these neural networks can be applied to real world problems.

TARGET AUDIENCE: Anyone interested in driving innovation

Published in: Data & Analytics
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An Introduction to Artificial Neural Networks

  1. 1. Computing systems vaguely inspired by the biological neural networks that constitute animal brains A class of deep, feed- forward artificial neural networks used to analyze visual imagery An interpreted high- level programming language for general- purpose programming Open Source frameworks are leading the charge into Neural Networks Tailored to your charitable and financial interests An open source machine learning framework for everyone
  2. 2. Kick off the talk
  3. 3. Software Development is my passion. I have almost 20 years of experience using Microsoft tools to develop software. Currently I am the Principal Cloud Architect at SafeNet Consulting, where I get to do what I love... Architect, Design, and Develop great software! I currently focus on Microservices, SOA, Azure, Neural Networks, and HoloLens. Cameron Vetter
  4. 4. ROAD MAP Theory + Examples Theory + Examples Theory + Examples Theory + Examples Theory + Examples REVIEW
  5. 5. What is a Neural Network and why would I use one?
  6. 6. The Brains Neuron Parts Dendrites = Input Soma = Signal Processing Axon = Output
  7. 7. The ANN Neuron Parts Input Activation Function Output
  8. 8. Backpropagation
  9. 9. Gradient descent Definition A first-order iterative optimization algorithm for finding the minimum of a function. To find a local minimum of a function using gradient descent, one takes steps proportional to the negative of the gradient of the function at the current point.
  10. 10. Customer Retention (Bank) • Input • Credit Rating • Income Level • Gender • Age • Number of Products • Balance • Output • Likelihood of Customer Closing Accounts within 3 months
  11. 11. Source: https://www.youtube.com/watch?v=tRdnoFb-eZA Handwriting Recognition
  12. 12. Loan Approval
  13. 13. CNN’s use feature detection and are often used for Image and Video Recognition
  14. 14. Convolutional Neural Network
  15. 15. Convolution
  16. 16. Pooling Subsampling or Downsampling • Reduces Dimensionality • Retains Important Information • Common Algorithms • Max • Average • Sum
  17. 17. Flattening
  18. 18. Convolutional Neural Network
  19. 19. Image Recognition Source: https://www.microsoft.com/en-us/seeing-ai/
  20. 20. Text Classification Source: https://ieeexplore.ieee.org/document/4664350/ • Yahoo Answers question classification • Unclassified questions rarely received answers
  21. 21. Drug Discovery Source: https://www.atomwise.com/2015/12/02/introducing-atomnet-drug- design-with-convolutional-neural-networks/ AtomNet • Used to predict candidate treatments for Ebola • Prediction visualized resulted in molecule going into animal trials
  22. 22. A neural network that is trained through unsupervised learning using competitive learning
  23. 23. How Does it Self Organize? Source: www.cis.hut.fi
  24. 24. Why does it converge? Source: www.cis.hut.fi
  25. 25. An Example of SOM Epochs Source: https://algobeans.com/2017/11/02/self-organizing-map/
  26. 26. Categorizing Data Source: www.cis.hut.fi
  27. 27. Traveling Salesperson Problem Source: https://diego.codes/post/som-tsp/
  28. 28. Land Use Data Imputation Source: http://www.mdpi.com/2073-4441/7/12/6663/htm
  29. 29. An RNN forms a directed graph along a sequence, this allows it to exhibit temporal behavior
  30. 30. What Makes it Recurrent?
  31. 31. Unrolling Over Time
  32. 32. Long Short Term Memory Network
  33. 33. Music Composition Source: http://www.hexahedria.com/2015/08/03/composing-music- with-recurrent-neural-networks/ • Trained with random short music segments • Composes MIDI Output
  34. 34. Stock Market Prediction Source: stocksneural.net
  35. 35. Speech Recognition Source: https://azure.microsoft.com/en-us/services/cognitive- services/speech-to-text/
  36. 36. Autoencoders learn to compress data from the input layer into short code and uncompress that code into something resembling the original input
  37. 37. Structure
  38. 38. How Does it Work? Source: https://probablydance.com/2016/04/30/neural-networks-are- impressively-good-at-compression/ 0 1 0 0 0
  39. 39. Denoising Images / Audio Source: https://towardsdatascience.com/applied-deep-learning-part-3- autoencoders-1c083af4d798
  40. 40. Medical Image Denoising Source: https://arxiv.org/pdf/1608.04667.pdf Original Noisy Denoised
  41. 41. Silicon Valley Not Hotdog App Source: Silicon Valley / HBO
  42. 42. A Quick Review
  43. 43. ROAD MAP Theory + Examples Theory + Examples Theory + Examples Theory + Examples Theory + Examples REVIEW
  44. 44. @Poshporcupine linkedin.com/in/cameronvetterwww.cameronvetter.com Any Questions?

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