机器视觉在深学习神经网络中的应用

　　机器视觉在深学习神经网络中的应用

　　人工智能（AI）长期以来一直是科幻小说作家和学者的主题。将人脑的复杂性复制到计算机中的挑战催生了新一代的科学家、数学家和计算机算法开发人员。持续的研究已经让位给使用人工智能，更经常被称为深学习或机器学习，越来越多的应用程序的一部分，我们的世界。虽然基本概念已经存在了很长一段时间，商业现实从来没有完全实现。在最近几年中，数据生成的速度急剧上升，开发者不得不思考如何编写算法来从中提取有价值的数据和统计数据。此外，另一个关键因素是高可用性的计算资源水平，云已经心甘情愿地取得了。例如，在你的口袋里的智能手机可能会使用谷歌的现在（好谷歌）或苹果的Siri语音命令的应用程序，使用深度学习算法的功率，称为（人工）神经网络，使语音识别和学习能力。然而，除了与你的电话交谈的乐趣和方便，有一系列的工业，汽车和商业应用，现在受益于深学习神经网络的力量。

　　The convolutional neural network

　　Before taking a look at some of those applications， let’s investigate how a neural network works and what resources it needs. When we talk generally about neural networks we should more accurately describe them as artificial neural networks. Implemented as software algorithms， they are based on the biological neural networks – the central nervous system – of humans and animals. There have been several different types of neural network architecture conceived over the years of which the convolutional neural network （CNN） has been the most widely adopted. One of the key reasons for this has been that their architectural approach makes them well suited for using the parallelization techniques offered by hardware accelerators based around GPU and FPGA devices. Another reason for the popularity of CNNs is that they suit working with data that has a lot of spatial continuity， of which image processing applications are a perfect fit. Spatial continuity refers to pixels in the vicinity of a specific location sharing similar intensity and color attributes. CNNs are architected out of different layers， each having a specific purpose， and there are two distinct phases used in their operation. The first part is an instruction， or training phase that allows the processing algorithm to understand what data it has and the relationship， or context between each piece of data. The CNN is created as a learning framework from the structured and unstructured data， with computer created neurons forming the network of connections and breaks. Pattern matching is a key concept behind a CNN， something that is used extensively in machine learning.