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Beating CAPTCHAs with Neural Networks
The outputs of one layer become the inputs of the next layer, continuing until we
reach the final layer: the, output layer. These outputs represent the predictions of
the neural network as the classification. Any layer of neurons between the
input layer and the output layer is referred to as a hidden layer, as they learn a
representation of the data not intuitively interpretable by humans. Most neural
networks have at least three layers, although most modern applications use networks
with many more layers than that.
Typically, we consider fully connected layers. The outputs of each neuron in a layer
go to all neurons in the next layer. While we do define a fully connected network,
many of the weights will be set to zero during the training process, effectively
removing these links. Fully connected neural networks are also simpler and more
efficient to program than other connection patterns.
As the function of the neurons is normally the logistic function, and the neurons are
fully connected to the next layer, the parameters for building and training a neural
network must be other factors. The first factor for neural networks is in the building
phase: the size of the neural network. This includes how many layers the neural
network has and how many neurons it has in each hidden layer (the size of the
input and output layers is usually dictated by the dataset).
The second parameter for neural networks is determined in the training phase: the
weight of the connections between neurons. When one neuron connects to another,
this connection has an associated weight that is multiplied by the signal (the output
of the first neuron). If the connection has a weight of 0.8, the neuron is activated, and
it outputs a value of 1, the resulting input to the next neuron is 0.8. If the first neuron
is not activated and has a value of 0, this stays at 0.
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