## Neural Networks and Deep Learning

It has been a long time since the idea of neural networks was proposed, but it is really during the last few years that neural networks have become widely used. One of the major enablers is the infrastructure with high computational capability (e.g., cloud computing), which makes the training ...

read more## Latent Dirichlet Allocation and Topic Modeling

When reading an article, we humans are able to easily identify the topics the article talks about. An interesting question is: can we automate this process, i.e., train a machine to find out the underlying topics in articles? In this post, a very popular topic modeling method, Latent Dirichlet ...

read more## Hidden Markov Model and Part of Speech Tagging

In a Markov model, we generally assume that the states are directly observable or one state corresponds to one observation/event only. However, this is not always true. A good example would be: in speech recognition, we are supposed to identify a sequence of words given a sequence of utterances ...

read more## Expectation Maximization Algorithm and Gaussian Mixture Model

In statistical modeling, it is possible that some observations are just missing. For example, when flipping two biased coins with unknown biases, we only have a sequence of observations on heads and tails, but forgot to record which coin each observation comes from. In this case, the conventional maximum likelihood ...

read more## Binary and Multiclass Logistic Regression Classifiers

The generative classification model, such as Naive Bayes, tries to learn the probabilities and then predict by using Bayes rules to calculate the posterior, \(p(y|\textbf{x})\). However, discrimitive classifiers model the posterior directly. As one of the most popular discrimitive classifiers, logistic regression directly models the linear decision ...

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