Neural Network Depth

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A Neural Network Depth is a network topology metric that accounts for the number of hidden layers.



References

2017

  • The second network (right) has [math]\displaystyle{ 4 + 4 + 1 = 9 }[/math] neurons, [math]\displaystyle{ [3 \times 4] + [4 \times 4] + [4 \times 1] = 12 + 16 + 4 = 32 }[/math] weights and [math]\displaystyle{ 4 + 4 + 1 = 9 }[/math] biases, for a total of 41 learnable parameters.

    To give you some context, modern Convolutional Networks contain on orders of 100 million parameters and are usually made up of approximately 10-20 layers (hence deep learning).

Left: A 2-layer Neural Network (one hidden layer of 4 neurons (or units) and one output layer with 2 neurons), and three inputs.

Right:A 3-layer neural network with three inputs, two hidden layers of 4 neurons each and one output layer. Notice that in both cases there are connections (synapses) between neurons across layers, but not within a layer.


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