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Neural Networks and Deep Learning (Part 1 of 2): An introduction - Valentino Zocca, Real Data Machines

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In this talk we will introduce artificial neural networks and their similarities to how the brain works. We will provide some history and theoretical foundations to introduce feed-forward multi-layer neural networks, describing their predictive and learning ability, and some of their applications in the real world. This is part one of an introductory pair of talks on deep learning concepts and theory.

Valentino Zocca has a Ph.D. in Mathematics from the University of Maryland with a thesis in theoretical geometry, though he has since worked on technical applications and first-on-the block VR geo-navigation data tools and data analysis. Currently he lives in Italy and the United States where he works on emerging deep learning protocols and neural network architectures.

Published in: Data & Analytics
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Neural Networks and Deep Learning (Part 1 of 2): An introduction - Valentino Zocca, Real Data Machines

  1. 1. An introduction to neural nets by Valentino Zocca
  2. 2. Deep Learning in AI Digitalisation Features Classification/Prediction AnalysisInteraction The World Sensor Machine Learning
  3. 3. 1642 • Pascaline: first mechanical adder, invented by French mathematician Blaise Pascal, using a system of gears and wheels could add and subtract numbers.
  4. 4. 1694 • Machine by Gottfried Wilhelm Von Leibniz who also developed calculus and invented the binary system. His machine could also multiplicate and divide.
  5. 5. 1801 • Joseph Marie Charles invents the Jacquard loom to weave different patterns using cards punched with holes. Precursor for modern computers and data storage.
  6. 6. 1890 • Herman Hollerith, founder of the Tabulating Machine Company (later merged into IBM), creates a mechanical tabulator using punched cards to store data to calculate statistics. He is regarded as the father of modern machine data processing.
  7. 7. 1957 • Frank Rosenblatt invents the perceptron algorithm, the first neural networks implementation. It was later proved by Marvin Minsky and Seymour Papert in 1969 that it could not learn the XOR function.
  8. 8. 1974 • Paul Werbos’s Ph.D. thesis describes the process of training neural nets through back- propagation.
  9. 9. Supervised Learning 1. Input Data 2. Process the information 3. Check the output 4. Learn new rule 5. New rule is applied to better performance
  10. 10. Neural Networks The theory of neural networks arises from the attempt to mimic our biological brain in order to create machines that can learn or perform pattern recognition in order to make predictions.
  11. 11. Neural Networks Neural networks are systems comprised of many ”neurons”, which are the units of the neural net.
  12. 12. Neural Networks In neural networks, a space of ”weights” is defined alongside the input space. The weights and the input together define the activity rules that in turn will define the output according to specified activation functions.
  13. 13. Neural Networks The weights can change with time as the neural network learns and their change may be specified by some learning rule which will generally be depending on the activities of the neurons.
  14. 14. Perspective • Biggest artificial neural network to-date: Over 11 billion parameters. (1.1 * 10ˆ10). • Number of neurons in brain 10ˆ11, each with about 10ˆ4 connections for a total of 10ˆ15 parameters.
  15. 15. A model for a neuron Images from upcoming book "Python Deep Learning"
  16. 16. Neural Networks x w wx
  17. 17. Neural Networks x w1 w1x+w2y y w2
  18. 18. Neural Networks x w1 w1x+w2y+b y w2 1 b w1x+w2y+b > 0 w1x+w2y > -b
  19. 19. Neural Networks x w1 σ(w1x+w2y+b) y w2 1 b
  20. 20. Neural Networks
  21. 21. The Universal Approximation Theorem Neural networks with a single hidden layer can be used to approximate any continuous function to any desired precision.
  22. 22. A logistic sigmoid: 1/(1+exp(-x)) In general: 1/(1+exp(-wx-b))
  23. 23. Why the Universal Approximation theorem is true X n1 n2 Y w -w
  24. 24. A step function
  25. 25. Classic Neural Networks Picture by John Kaufhold
  26. 26. Deep neural networks in the 80’s Recent Developments in Deep Learning by Geoff Hinton https://www.youtube.com/watch?v=vShMxxqtDDs
  27. 27. Learning Representations Neural Networks for Machine Learning by Geoff Hinton https://class.coursera.org/neuralnets-2012-001/lecture
  28. 28. Learning Representations Neural Networks for Machine Learning by Geoff Hinton https://class.coursera.org/neuralnets-2012-001/lecture
  29. 29. Building High-level Features Using Large Scale Unsupervised Learning http://research.google.com/archive/unsupervised_icml2012.html
  30. 30. http://ai.stanford.edu/~ang/papers/icml09-ConvolutionalDeepBeliefNetworks.pdf
  31. 31. http://ai.stanford.edu/~ang/papers/icml09-ConvolutionalDeepBeliefNetworks.pdf
  32. 32. http://ai.stanford.edu/~ang/papers/icml09-ConvolutionalDeepBeliefNetworks.pdf
  33. 33. http://ai.stanford.edu/~ang/papers/icml09-ConvolutionalDeepBeliefNetworks.pdf
  34. 34. http://ai.stanford.edu/~ang/papers/icml09-ConvolutionalDeepBeliefNetworks.pdf
  35. 35. http://ai.stanford.edu/~ang/papers/icml09-ConvolutionalDeepBeliefNetworks.pdf
  36. 36. http://ai.stanford.edu/~ang/papers/icml09-ConvolutionalDeepBeliefNetworks.pdf
  37. 37. Picture by John Kaufhold http://www.cs.toronto.edu/~rsalakhu/ISBI1_pdf_version.pdf
  38. 38. Next Talk (Part II) • Ising Models • Restricted Boltzmann Machines • Convolutional Networks
  39. 39. Various Resources Courses • Geoff Hinton Coursera course- http://www.coursera.org/course/neu ralnets) • Andrew Ng Coursera course- http://www.coursera.org/course/ml
  40. 40. An introduction to neural nets by Valentino Zocca

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