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Intro to Neural Networks

Supervised Learning: Perceptrons and Backpropagation. Intro to Neural Networks. Neural Network ==. Connectionist /ism== Parallel Distributed Processing (PDP). Neural Networks assume . Intelligence is emergent. 1943 - McCullough Pitts Artificial Neuron.

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Intro to Neural Networks

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  1. Supervised Learning: Perceptrons and Backpropagation Intro to Neural Networks

  2. Neural Network == • Connectionist /ism== • Parallel Distributed Processing (PDP)

  3. Neural Networks assume • Intelligence is emergent

  4. 1943 - McCullough Pitts Artificial Neuron

  5. 1943 - McCullough Pitts Artificial Neuron

  6. Perceptron Learning 1958

  7. Perceptron Learning 1958

  8. Perceptron Learning 1958

  9. Perceptron Learning 1958

  10. Perceptron Learning 1958

  11. Linear Seperability Problem 1965

  12. Linear Seperability Problem 1965

  13. Backpropagation • Used to train multilayer feedforward networks

  14. Backpropagation

  15. Backpropagation • Used to train multilayer feedforward networks • Assumes a continuous activation function

  16. Backpropagation - Activation

  17. Backpropagation • Used to train multilayer feedforward networks • Assumes a continuous activation function • Delta rule

  18. Backpropagation Delta rule • Perceptron update rule was: • Backprop update rule is:

  19. Backpropagation Delta rule • Error of an output node:

  20. Backpropagation Delta rule • Error of a hidden node:

  21. Backpropagation Delta rule

  22. Backpropagation Delta rule

  23. Backpropagation Delta rule

  24. Backpropagation Delta rule

  25. Backpropagation • demo

  26. Inductive Bias

  27. Inductive Bias • Encoding / Feature Extraction • # neurons used • # layers used • Output mapping

  28. Domains • Classification

  29. Domains • Classification • Pattern Recognition

  30. Domains • Classification • Pattern Recognition • Content Addressable Memory

  31. Domains • Classification • Pattern Recognition • Content Addressable Memory • Prediction

  32. Domains • Classification • Pattern Recognition • Content Addressable Memory • Prediction • Optimization

  33. Domains • Classification • Pattern Recognition • Content Addressable Memory • Prediction • Optimization • Filtering

  34. The good • Degrade gracefully

  35. The good • Degrade gracefully • Solve ill-defined problems

  36. The good • Degrade gracefully • Solve ill-defined problems • Flexible

  37. The good • Degrade gracefully • Solve ill-defined problems • Flexible • Generalization

  38. The bad • Time & Memory

  39. The bad • Time & Memory • Black box

  40. The bad • Time & Memory • Black box • Trial and Error

  41. When not to use Feedforward net • If you can draw a flow chart or formula

  42. When not to use Feedforward net • If you can draw a flow chart or formula • If a piece of hardware or software already exists that does what you want

  43. When not to use Feedforward net • If you can draw a flow chart or formula • If a piece of hardware or software already exists that does what you want • If you want to functionality to evolve

  44. When not to use Feedforward net • If you can draw a flow chart or formula • If a piece of hardware or software already exists that does what you want • If you want to functionality to evolve • Precise answers are required

  45. When not to use Feedforward net • If you can draw a flow chart or formula • If a piece of hardware or software already exists that does what you want • If you want to functionality to evolve • Precise answers are required • The problem could be described in a lookup table

  46. When to use feedforward net • You can define a correct answer

  47. When to use feedforward net • You can define a correct answer • You have a lot of training data with examples of right and wrong answers

  48. When to use feedforward net • You can define a correct answer • You have a lot of training data with examples of right and wrong answers • You have lots of data but can’t figure how to map it to output

  49. When to use feedforward net • You can define a correct answer • You have a lot of training data with examples of right and wrong answers • You have lots of data but can’t figure how to map it to output • The problem is complex but solvable

  50. When to use feedforward net • You can define a correct answer • You have a lot of training data with examples of right and wrong answers • You have lots of data but can’t figure how to map it to output • The problem is complex but solvable • The solution is fuzzy or might change slightly

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