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Technological Educational Institute Of Crete Department Of Applied Informatics and Multimedia

Technological Educational Institute Of Crete Department Of Applied Informatics and Multimedia Intelligent Systems Laboratory. Technological Educational Institute Of Crete Department Of Applied Informatics and Multimedia Intelligent Systems Laboratory.

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Technological Educational Institute Of Crete Department Of Applied Informatics and Multimedia

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  1. Technological Educational Institute Of Crete Department Of Applied Informatics and Multimedia Intelligent Systems Laboratory

  2. Technological Educational Institute Of CreteDepartment Of Applied Informatics and MultimediaIntelligent Systems Laboratory

  3. Technological Educational Institute Of CreteDepartment Of Applied Informatics and MultimediaIntelligent Systems Laboratory • Networks without hidden units are very limited in the input-output mappings they can model. • More layers of linear units do not help. Its still linear. • Fixed output non-linearities are not enough • We need multiple layers of adaptive non-linear hidden units. This gives us a universal approximator. But how can we train such nets? • We need an efficient way of adapting all the weights, not just the last layer. This is hard. Learning the weights going into hidden units is equivalent to learning features. • Nobody is telling us directly what hidden units should do.

  4. Technological Educational Institute Of CreteDepartment Of Applied Informatics and MultimediaIntelligent Systems Laboratory

  5. Technological Educational Institute Of CreteDepartment Of Applied Informatics and MultimediaIntelligent Systems Laboratory

  6. Technological Educational Institute Of CreteDepartment Of Applied Informatics and MultimediaIntelligent Systems Laboratory

  7. Technological Educational Institute Of CreteDepartment Of Applied Informatics and MultimediaIntelligent Systems Laboratory

  8. Technological Educational Institute Of CreteDepartment Of Applied Informatics and MultimediaIntelligent Systems Laboratory

  9. Technological Educational Institute Of CreteDepartment Of Applied Informatics and MultimediaIntelligent Systems Laboratory

  10. Technological Educational Institute Of CreteDepartment Of Applied Informatics and MultimediaIntelligent Systems Laboratory

  11. Technological Educational Institute Of CreteDepartment Of Applied Informatics and MultimediaIntelligent Systems Laboratory

  12. 4. The actual output of the network is compared to expected output for that particular input. This results in an error value.. The connection weights in the network are gradually adjusted, working backwards from the output layer, through the hidden layer, and to the input layer, until the correct output is produced. Fine tuning the weights in this way has the effect of teaching the network how to produce the correct output for a particular input, i.e. the network learns. Technological Educational Institute Of CreteDepartment Of Applied Informatics and MultimediaIntelligent Systems Laboratory

  13. Technological Educational Institute Of CreteDepartment Of Applied Informatics and MultimediaIntelligent Systems Laboratory • The delta rule is often utilized by the most common class of ANNs called backpropagational neural networks.

  14. Technological Educational Institute Of CreteDepartment Of Applied Informatics and MultimediaIntelligent Systems Laboratory

  15. Technological Educational Institute Of CreteDepartment Of Applied Informatics and MultimediaIntelligent Systems Laboratory

  16. Technological Educational Institute Of CreteDepartment Of Applied Informatics and MultimediaIntelligent Systems Laboratory

  17. Technological Educational Institute Of CreteDepartment Of Applied Informatics and MultimediaIntelligent Systems Laboratory • A set of examples for training the network is assembled. Each case consists of a problem statement (which represents the input into the network) and the corresponding solution (which represents the desired output from the network). • The input data is entered into the network via the input layer. • Each neuron in the network processes the input data with the resultant values steadily "percolating" through the network, layer by layer, until a result is generated by the output layer.

  18. Technological Educational Institute Of CreteDepartment Of Applied Informatics and MultimediaIntelligent Systems Laboratory

  19. Technological Educational Institute Of CreteDepartment Of Applied Informatics and MultimediaIntelligent Systems Laboratory

  20. Technological Educational Institute Of CreteDepartment Of Applied Informatics and MultimediaIntelligent Systems Laboratory

  21. Technological Educational Institute Of CreteDepartment Of Applied Informatics and MultimediaIntelligent Systems Laboratory

  22. Technological Educational Institute Of CreteDepartment Of Applied Informatics and MultimediaIntelligent Systems Laboratory

  23. Technological Educational Institute Of CreteDepartment Of Applied Informatics and MultimediaIntelligent Systems Laboratory

  24. Technological Educational Institute Of CreteDepartment Of Applied Informatics and MultimediaIntelligent Systems Laboratory

  25. Technological Educational Institute Of CreteDepartment Of Applied Informatics and MultimediaIntelligent Systems Laboratory

  26. Technological Educational Institute Of CreteDepartment Of Applied Informatics and MultimediaIntelligent Systems Laboratory

  27. Technological Educational Institute Of CreteDepartment Of Applied Informatics and MultimediaIntelligent Systems Laboratory

  28. Technological Educational Institute Of CreteDepartment Of Applied Informatics and MultimediaIntelligent Systems Laboratory

  29. Technological Educational Institute Of CreteDepartment Of Applied Informatics and MultimediaIntelligent Systems Laboratory

  30. Technological Educational Institute Of CreteDepartment Of Applied Informatics and MultimediaIntelligent Systems Laboratory

  31. Technological Educational Institute Of CreteDepartment Of Applied Informatics and MultimediaIntelligent Systems Laboratory

  32. Technological Educational Institute Of CreteDepartment Of Applied Informatics and MultimediaIntelligent Systems Laboratory

  33. Technological Educational Institute Of CreteDepartment Of Applied Informatics and MultimediaIntelligent Systems Laboratory

  34. Technological Educational Institute Of CreteDepartment Of Applied Informatics and MultimediaIntelligent Systems Laboratory

  35. Technological Educational Institute Of CreteDepartment Of Applied Informatics and MultimediaIntelligent Systems Laboratory

  36. Technological Educational Institute Of CreteDepartment Of Applied Informatics and MultimediaIntelligent Systems Laboratory

  37. Technological Educational Institute Of CreteDepartment Of Applied Informatics and MultimediaIntelligent Systems Laboratory

  38. Technological Educational Institute Of CreteDepartment Of Applied Informatics and MultimediaIntelligent Systems Laboratory

  39. Technological Educational Institute Of CreteDepartment Of Applied Informatics and MultimediaIntelligent Systems Laboratory

  40. Technological Educational Institute Of CreteDepartment Of Applied Informatics and MultimediaIntelligent Systems Laboratory

  41. Technological Educational Institute Of CreteDepartment Of Applied Informatics and MultimediaIntelligent Systems Laboratory

  42. Technological Educational Institute Of CreteDepartment Of Applied Informatics and MultimediaIntelligent Systems Laboratory

  43. Technological Educational Institute Of CreteDepartment Of Applied Informatics and MultimediaIntelligent Systems Laboratory

  44. Technological Educational Institute Of CreteDepartment Of Applied Informatics and MultimediaIntelligent Systems Laboratory

  45. Technological Educational Institute Of CreteDepartment Of Applied Informatics and MultimediaIntelligent Systems Laboratory

  46. The error surface itself is a hyperparaboloid but is seldom smooth. Indeed, in most problems, the solution space is quite irregular with numerous pits and hills which may cause the network to settle down in a local minimum which is not the best overall solution. Technological Educational Institute Of CreteDepartment Of Applied Informatics and MultimediaIntelligent Systems Laboratory

  47. Technological Educational Institute Of CreteDepartment Of Applied Informatics and MultimediaIntelligent Systems Laboratory

  48. Technological Educational Institute Of CreteDepartment Of Applied Informatics and MultimediaIntelligent Systems Laboratory

  49. Technological Educational Institute Of CreteDepartment Of Applied Informatics and MultimediaIntelligent Systems Laboratory

  50. Technological Educational Institute Of CreteDepartment Of Applied Informatics and MultimediaIntelligent Systems Laboratory

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