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Chapter 8
The combination of an appropriately sized network and well-trained weights
determines how accurate the neural network can be when making classifications. The
word "appropriately" also doesn't necessarily mean bigger, as neural networks that are
too large can take a long time to train and can more easily overfit the training data.
Weights are normally set randomly to start with, but are then
updated during the training phase.
We now have a classifier that has initial parameters to set (the size of the network)
and parameters to train from the dataset. The classifier can then be used to predict the
target of a data sample based on the inputs, much like the classification algorithms we
have used in previous chapters. But first, we need a dataset to train and test with.
Creating the dataset
In this chapter, we will take on the role of the bad guy. We want to create a program
that can beat CAPTCHAs, allowing our comment spam program to advertise on
someone's website. It should be noted that our CAPTCHAs will be a little easier
that those used on the web today and that spamming isn't a very nice thing to do.
Our CAPTCHAs will be individual English words of four letters only, as shown in
the following image:
Our goal will be to create a program that can recover the word from images like this.
To do this, we will use four steps:
1. Break the image into individual letters.
2. Classify each individual letter.
3. Recombine the letters to form a word.
4. Rank words with a dictionary to try to fix errors.
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