Neural Netwerking with Annie
Neural networks are computer programs that are loosely
inspired by biological neurology. Brains consist of
neurological cells or neurons and connections between
neurons. Neurons communicate with each other by sending
short electrical pulses.
You can read more on the
working of the mammalian brain here. Neural
Networks (or Artificial Neural Networks) are a
crude imitiation of the basic working of the brain.
| Neural networks consist of neurons and connections between neurons, but that
is more or less where the anology halts. Neural networks have learning mechanisms
or algorithms that have very little in common with biological learning algorithms.
|As a research subject neural networks are very interesting; there are many questions
surrounding neural networks, many of them fundamental. There are also many variables in
training neural networks, which makes training a neural network a non-trivial task.
|The software tool ANNIE (A Neural Network Interaction Invironment) was developed as an
exploration tool for neural networks. It aids in training neural networks, and gives feedback
on weight updates between connections and the overall error of the network. It also helps
in discovering what neural networks are good at. Currently I am using ANNIE to train
neural networks with training sets from the
University of California at Irvine (UCI)
|There are a number of issues I want to implement in the near future;
first of all I want to implement the ability to train on image data
(two dimensional data), and possibly produce image data as output.
This way I could train a network to perform Image
segmentation or any other image manipulation task (I could
train my own Photoshop plugins). I might create an integration with
with my art generation project Argento.
Next I want to integrate neural networks with genetic algorithms,
in order to to be able to evolve optimal neural network
configurations. At last I want to implement the persistence of
neural networks from and to XML using the Predicitive Model Markup
Language (PMML v3.0) format. This way it will possible to train
a certain NN setup with a data set and exchange the settings with
anyone else who implements the PMML format (neural networks will
become free of the underlying implementation, which will enhance
reproducibilty of training results).
2000-2007, Eelco den Heijer, Amsterdam,
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