Neural Aesthetic W2 Class Notes

Features are:

  1. patterns in data
  2. implicit
  3. indicative of salient aspects of objects
  4. closely related to bias

Fitting

  • Linear regression doesn’t give much flexibility; you can give a neuron more by outputting it through a non-linearity, ie a sigmoid function
    • ReLU (rectified linear unit) is preferred over a sigmoid function
  • adding a hidden layer gives y (the output) even more flexibility

Convolutional NNs: scans for certain patterns throughout the entire image

activation= value of the neuron

weights on the connections

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