intro to neural networks




activation function:

  • we add positive features and subtract negative features multiplied by their weight

  • have to pick a normalization function too (i.e. sigmoid, etc)


  1. takes the inputs and multiplies them by their weights

  2. sums them up

  3. applies the activation function to the sum



  • data passes through the different input nodes till it reaches the output node

    • moves in only one direction from the first tier onwards until it reaches the output node

    • this is also known as a front propagated wave which is usually achieved by using a classifying activation function

  • the sum of the products of the inputs and their weights are calculated

radial basis

  • distance of any point relative to the centre

  • 2 layers

    • inner: the features are combined with the radial basis function

    • output of these features is taken into account when calculating the same output in the next time-step

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