There is a nice tutorial here [1] about max-ent classifiers. Ultimately in neural networks there are usually a number of cost functions you can use for the last

There is a nice tutorial here [1] about max-ent classifiers. Ultimately in neural networks there are usually a number of cost functions you can use for the last layer - softmax or cross-entropy are other possibilities that may be easier to understand, though possibly less performant for NLP tasks. I thought the introduction to neural networks from Andrew Ng's coursera course (even though it meant writing MATLAB) was quite good, and allows you to implement backprop, cost functions, etc. while still having some other helper code to make things easier. I highly recommend working through that course if you are intersted in ML in general [2]. [1] http://www.cs.berkeley.edu/~klein/papers/maxent-tutorial-slides.pdf http://www.cs.berkeley.edu/~klein/papers/maxent-tutorial-sli... [2] https://www.coursera.org/course/ml https://www.coursera.org/course/ml