Using images in trading

There are some things computers can't really do. They can't enjoy a good burger. They don't seem to understand the importance of knafa. Ok, perhaps these aren't really that important. However, people are still pretty good at doing many things, that computers haven't been able to do well, at least until recently. Any toddler can recognise simple common objects like a car or a house. Yet, computers did not seem to be that good at what seemed to be a relatively trivial task for a human.

Of course, this has now all changed. Computers have become incredibly accurate when it comes to image recognition. One of the reasons is that we now guide computers to approach the problem in a different way to humans. Typically, when we try to recognise something within an image we use obvious features. For example, if we're trying to recognise a rabbit, we'll look for something with big ears etc. Traditionally, we've programmed computers to look for features such as edges, corners etc. in an image. In other words we need to specify and "handcraft" the features. 

In recent years, convolutional neural nets (CNN) and similar models have been used. Rather than having handcrafted features, we let the algorithm to discover higher order features during the training process, which will involve feeding it a large number of photos for training...

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