機器視覺在深學習神經網絡中的應用

　　人工智能（AI）長期以來一直是科幻小說作家和學者的主題。將人腦的復雜性復制到計算機中的挑戰催生了新一代的科學家、數學家和計算機算法開發人員。持續的研究已經讓位給使用人工智能，更經常被稱為深學習或機器學習，越來越多的應用程序的一部分，我們的世界。雖然基本概念已經存在了很長一段時間，商業現實從來沒有完全實現。在最近幾年中，數據生成的速度急劇上升，開發者不得不思考如何編寫算法來從中提取有價值的數據和統計數據。此外，另一個關鍵因素是高可用性的計算資源水平，云已經心甘情愿地取得了。例如，在你的口袋里的智能手機可能會使用谷歌的現在（好谷歌）或蘋果的Siri語音命令的應用程序，使用深度學習算法的功率，稱為（人工）神經網絡，使語音識別和學習能力。然而，除了與你的電話交談的樂趣和方便，有一系列的工業，汽車和商業應用，現在受益于深學習神經網絡的力量。

　　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.