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How Neural Network Works
Now that we know what neural network is, we will see the common terms used in neural network as we progress through this article.
Neuron/node: a node in a neural network which contains a value, usually a real number from 0 to 1
Layer: a set of neurons. A neural network model comprises layers: input layer, hidden layer, and output layer
As already seen from previous image, a neural network is divided into 3 layers: input layer, hidden layer, and output layer. The input layer receives a set of inputs in the form of numbers that would be contained in the input nodes. While the output later contains a set of nodes whose values determine the output of the neural network. Meanwhile, the hidden layer is a set of layers of nodes that truly make up the ‘network’. Every node in a particular layer is connected to every other nodes in the previous layer and/or the next layer. The image above should give you a hint: a node in input layer is connected to every nodes in a layer after it (hidden layer), while a node in the hidden layer is connected to every node in the previous layer (input layer) and the next layer (output layer). In real practice, the hidden layer may not only consists of one single layer, so a layer in the hidden layer does not directly act as a ‘bridge’ between the input layer and the output layer.
So far we had have a clear visualization of a NN model.
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