For the generation of test samples, it is sometimes necessary to only use truncated values, since they have to be in a specific range. Achieving this for values following a normal distribution is not straightforward to implement efficiently since the distribution should be left unchanged.
Posts tagged as Machine_Learning.
- Tagged as: Machine_Learning
Recently I had to deal with probabilities for vectors in python. Surprisingly, handling them is not straightforward, for practical and numerical reasons. I want to quickly sum up my findings here.Tagged as: Machine_Learning
I prepared a basic introductory python script for teaching. It shows how a perceptron can be created and how a basic feed-forward two-layer network can be used to learn to approximate the XOR function. I also included code to visualize the decision boundary of the ANNs during training.
While ANNs are a fascinating research subject, quickly implementing new algorithms using their concepts is not always easy. Speed matters, and there are many paradigms how ANN theory can be mapped to implementations. Currently working on extending my difference based learning theory introduced in this recent blog post to convolutional …
The following article describes work in progress. It might be published in a more complete form at a conference or journal. If you want to use this technique, please quote the web article.
Update (12/13/2013): The work on distance based ANN training has been published this December at …
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