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Neural Networks Projects

Neural networks are an important (or at least popular) field in artificial intelligence research. To better understand them, it is useful to implementing them, and do some experiments with training them. So far, I've implemented the following models:

Feedforward neural networks

The feedforward neural networks with the back-propagation algorithm are probably the most popular type of neural networks. I've implemented this type of networks once in Java as an assignment project for my Java classes. Later, I've created another implementation in Visual Basic .NET as a custom development project. Both implementation were created as object libraries, with focus on modularity and extensibility. Each part of the network - including the architecture, transition function and learning algorithms - can be changed according to your needs.

Palette reduction with Kohonen networks

While there certainly are better ways how to reduce the number of colors used in an image, this was a fun (school) project to work on. It was implemented as a Kohonen self-organizing map in the color space, trained by the colors of the pixels in the original image. The project included a nice visualisation of the structure of the network and distribution of the neurons.

Dithering with Hopfield networks

When reducing the palette in an image to a small number of colors, sharp edges on the borders of different colors are one of the great problems. To overcome this problem, multiple dithering algorithms were developed. Again, while there are much better (and more efficient!) ways how to do dithering, in can also be done with Hopfield networks. Again, this was a school project - and it was a lecture on neural networks, not image processing.