Deep Learning - Aktueller Treiber des KI-Fortschritts



What’s a Neural Network?


Most introductory texts to Neural Networks brings up brain analogies when describing them. Without delving into brain analogies, I find it easier to simply describe Neural Networks as a mathematical function that maps a given input to a desired output.


Neural Networks consist of the following components


  • An input layer, x
  • An arbitrary amount of hidden layers
  • An output layer, ŷ
  • A set of weights and biases between each layer, W and b
  • A choice of activation function for each hidden layer, σ. In this tutorial, we’ll use a Sigmoid activation function.


The diagram below shows the architecture of a 2-layer Neural Network (note that the input layer is typically excluded when counting the number of layers in a Neural Network)

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