Constructing a Maximum Entropy Classifier for Sentence Extraction
Conjugate gradient descent is a variation on gradient descent. Gradient descent is a method of finding the minimum of a function by taking steps that reduce the error between the data and the model parameters. Conjugate gradient descent extends gradient descent by searching a plane, instead of a line. The plane is defined as a linear combination of the gradient vector and the previous descent step vector. Conjugate gradient descent is very good at finding the solutions to a set of sparse linear equations.

Gradient descent and variations are general methods for finding the best parameters. The best practice is to define your specific model and then call on a separate gradient descent package to search for optimal values (i.e., hyperparameters) for the model.