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I would really like to do some neural network stuff, but i really have trouble reading the math. For example, they have a maxent classifier in the slides but I

by anko 12y ago

I would really like to do some neural network stuff, but i really have trouble reading the math. For example, they have a maxent classifier in the slides but I'd have no idea how to convert that to code. Any resources to learn that stuff?

tst 12y ago
You can check out Machine Learning: An Algorithmic Perspective by Stephen Marsland[0] which takes a less math-driven approach to ML. The code is available online if you want to take a look at it (it's written in Python)[1]. [0]: http://www.crcpress.com/product/isbn/9781420067187 http://www.crcpress.com/product/isbn/9781420067187 [1]: http://seat.massey.ac.nz/personal/s.r.marsland/MLBook.html http://seat.massey.ac.nz/personal/s.r.marsland/MLBook.html
kastnerkyle 12y ago
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
davmre 12y ago
The Stanford deep learning tutorials (http://deeplearning.stanford.edu/wiki/index.php/UFLDL_Tutorial http://deeplearning.stanford.edu/wiki/index.php/UFLDL_Tutori...) have pretty good notes and exercises with detailed guidance / starter code. Depending on where exactly your hangups are, working through some of the coding exercises there might be useful.
Houshalter 12y ago
http://www.metacademy.org/ http://www.metacademy.org/ is the best resource for this.

by anko 12y ago

tst 12y ago

You can check out Machine Learning: An Algorithmic Perspective by Stephen Marsland[0] which takes a less math-driven approach to ML. The code is available online if you want to take a look at it (it's written in Python)[1]. [0]: http://www.crcpress.com/product/isbn/9781420067187 http://www.crcpress.com/product/isbn/9781420067187 [1]: http://seat.massey.ac.nz/personal/s.r.marsland/MLBook.html http://seat.massey.ac.nz/personal/s.r.marsland/MLBook.html

kastnerkyle 12y ago

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

davmre 12y ago

The Stanford deep learning tutorials (http://deeplearning.stanford.edu/wiki/index.php/UFLDL_Tutorial http://deeplearning.stanford.edu/wiki/index.php/UFLDL_Tutori...) have pretty good notes and exercises with detailed guidance / starter code. Depending on where exactly your hangups are, working through some of the coding exercises there might be useful.

Houshalter 12y ago

http://www.metacademy.org/ http://www.metacademy.org/ is the best resource for this.