<p>have a look at <a href="https://stackoverflow.com/questions/37889914/neural-net-projection-layers-for-word-representation">this answer here</a></p>

<p>It explains the difference between the hidden layer and the projection layer.</p>

<p>Referring to this <a href="http://mi.eng.cam.ac.uk/~gwb24/publications/mphil.thesis.pdf" rel="nofollow">thesis</a></p>

<p>Also, do read <a href="http://arxiv.org/pdf/1301.3781v3.pdf" rel="nofollow">this paper</a> by Tomas Mikolov and go through <a href="http://www.coling-2014.org/COLING%202014%20Tutorial-fix%20-%20Tomas%20Mikolov.pdf" rel="nofollow">this tutorial</a>.</p>

<p>this will really improve your understanding.</p>

<p>Hope this helps!</p>
Writing code for A Neural Probabilistic Language Model Bengio, 2003. Not able to understand the model
<p>I'm trying to write code for <a href="http://jmlr.org/papers/volume3/bengio03a/bengio03a.pdf" rel="nofollow">A Neural Probabilistic Language Model by yoshua Bengio, 2003</a>, but I'm not able to understand the connections between the input layer and projection matrix and between projection matrix and hidden layer.  I'm not able to get how exactly is the learning for word-vector representation taking place.</p>