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Chapter 11
Summary
In this chapter, we looked at using deep neural networks, specifically convolution
networks, in order to perform computer vision. We did this through the Lasagne
and nolearn packages, which work off Theano. The networks were relatively easy
to build with nolearn's helper functions.
The convolution networks were designed for computer vision, so it shouldn't
be a surprise that the result was quite accurate. The final result shows that
computer vision is indeed an effective application using today's algorithms
and computational power.
We also used a GPU-enabled virtual machine to drastically speed up the
process, by a factor of almost 10 for my machine. If you need extra power to run
some of these algorithms, virtual machines by cloud providers can be an effective
way to do this (usually for less than a dollar per hour)—just remember to turn
them off when you are done!
This chapter's focus was on a very complex algorithm. Convolution networks take
a long time to train and have many parameters to train. Ultimately, the size of the
data was small in comparison; although it was a large dataset, we can load it all in
memory without even using sparse matrices. In the next chapter, we go for a much
simpler algorithm, but a much, much larger dataset that can't fit in memory. This
is the basis of Big Data and it underpins applications of data mining in many large
industries such as mining and social networks.
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