/explore
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www.greaterwrong.com | ||
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minireference.com
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www.jefftk.com
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| | | | | An incredibly productive way of working with the world is to reduce a complex question to something that can be modeled mathematically and then do the math. The most common way this can fail, however, is when your model is missing important properties of the real world. Consider insurance: there's some event with probability X% under which you'd be out $Y, you want to maximize the logarithm of your wealth, and your current wealth is $Z. Under this model, you can calculate (more) the most you should be willing to pay to insure against this. | |
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achyutjoshi.github.io
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| | | | | Many would reckon that Machine Learning is now the new oil these days. And I would most likely support that. Personally I got exposed to the world of ML in m... | |
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www.unite.ai
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| | | Some machine learning models belong to either the generative or discriminative model categories. Yet what is the difference between these two categories of models? What does it mean for a model to be discriminative or generative? The short answer is that generative models are those that include the distribution of the data set, returning a [] | ||
