In order to understand how CRF works, it is important to understand the basic concepts like:
- what are probabilistic models
- what are graphical models
- basics of probability
- basic probabilistic models such as naive bayes, maximum entropy and HMM
This is the version 1.0 of the presentation I'm developing. I'm planning to add more details like Baum-Welch algorithm, Perceptron/SGD training algorithm, Regularization etc.
Stay tuned and feel free to suggest additional CRF topic to be included.