Learning Data Mining with Python

Beating CAPTCHAs with Neural Networks
• Extracting basic features from images
• Using neural networks for larger-scale classification tasks
• Improving performance using postprocessing
Artificial neural networks
Neural networks are a class of algorithm that was originally designed based on
the way that human brains work. However, modern advances are generally based
on mathematics rather than biological insights. A neural network is a collection of
neurons that are connected together. Each neuron is a simple function of its inputs,
which generates an output:
The functions that define a neuron's processing can be any standard function, such
as a linear combination of the inputs, and are called the activation function. For the
commonly used learning algorithms to work, we need the activation function to be
derivable and smooth. A frequently used activation function is the logistic function,
which is defined by the following equation (k is often simply 1, x is the inputs into
the neuron, and L is normally 1, that is, the maximum value of the function):
The value of this graph, from -6 to +6, is shown as follows:
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