了解对自然语言处理的卷积神经网络
当我们听到卷积神经网络（CNN）的时候，我们通常会想到计算机视觉。 CNN负责图像分类的重大突破，是当今大多数计算视觉系统的核心，从Facebook的自动照片标签到自动驾驶。

最近我们也开始将CNN应用于自然语言处理中的问题，并获得了一些有趣的结果。 在这篇文章中，我将尝试总结一下CNN是什么，以及它们如何在NLP中使用。 对于“计算机视觉”用例来说，CNN背后的直觉更容易理解，所以我将从那里开始，然后慢慢地向NLP移动。

Understanding Convolutional Neural Networks for NLP
When we hear about Convolutional Neural Network (CNNs), we typically think of Computer Vision. CNNs were responsible for major breakthroughs in Image Classification and are the core of most Computer Vision systems today, from Facebook’s automated photo tagging to self-driving cars.

More recently we’ve also started to apply CNNs to problems in Natural Language Processing and gotten some interesting results. In this post I’ll try to summarize what CNNs are, and how they’re used in NLP. The intuitions behind CNNs are somewhat easier to understand for the Computer Vision use case, so I’ll start there, and then slowly move towards NLP.

原文链接：http://www.wildml.com/2015/11/understanding-convolutional-neural-networks-for-nlp/

更多教程：http://www.tensorflownews.com/