中文译版：深度学习、自然语言处理和表征方法
英文原文：Deep Learning, NLP, and Representations
1：It’s true, essentially, because the hidden layer can be used as a lookup table.
2：word embeddings；
3：It seems natural for a network to make words with similar meanings have similar vectors.
4：You’ve seen all the words that you understand before, but you haven’t seen all the sentences that you understand before. So too with neural networks.
5：Word embeddings exhibit an even more remarkable property: analogies between words seem to be encoded in the difference vectors between words.
6：This general tactic – learning a good representation on a task A and then using it on a task B – is one of the major tricks in the Deep Learning toolbox. It goes by different names depending on the details: pretraining, transfer learning, and multi-task learning. One of the great strengths of this approach is that it allows the representation to learn from more than one kind of data.
There’s a counterpart to this trick. Instead of learning a way to represent one kind of data and using it to perform multiple kinds of tasks, we can learn a way to map multiple kinds of data into a single representation!
7：Shared Representations
（1）Bilingual Word Embeddings；
（2）Embed images and words in a single representation；
8：By merging sequences of words, A takes us from representing words to representing phrases or even representing whole sentences! And because we can merge together different numbers of words, we don’t have to have a fixed number of inputs.
【笔记】Deep Learning, NLP, and Representations
原文：http://www.cnblogs.com/CheeseZH/p/4369983.html