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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 above 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)
read the rest of this article with code examples at https://towardsdatascience.com/how-to-build-your-own-neural-network-from-scratch-in-python-68998a08e4f6
