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distill.pub | ||
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colah.github.io
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fanpu.io
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| | | | | Deep learning is currently dominated by parametric models, which are models with a fixed number of parameters regardless of the size of the training dataset. Examples include linear regression models and neural networks. However, it's good to occasionally take a step back and remember that that is not all there is. Non-parametric models like k-NN, decision trees, or kernel density estimation don't rely on a fixed set of weights, but instead grow in complexity based on the size of the data. In this post we'll talk about Gaussian processes, a conceptually important, but in my opinion under-appreciated non-parametric approach with deep connections with modern-day neural networks. An intersting motivating fact which we will eventually show is that neural network... | |
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pberba.github.io
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| | | | | What is HDBSCAN and why does it work | |
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akosiorek.github.io
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| | | Machine learning is all about probability.To train a model, we typically tune its parameters to maximise the probability of the training dataset under the mo... | ||
