深度学习中的深度是指什么
Data science is revolutionizing many fields; from robotics to medicine, and everything in between. This revolution is partly due to advances in research, computing power, interests within the field, and the data science toolbox. Often, persons think of data science as extreme advances within artificial intelligence (AI); as in, eventually giving robots the ability to complete human-dominated tasks all on their own.
数据科学正在革新许多领域。 从机器人技术到医学，以及介于两者之间的一切。 这场革命部分是由于研究，计算能力，该领域内的兴趣以及数据科学工具箱的进步。 人们常常将数据科学视为人工智能(AI)的一项极端进步。 就像这样，最终使机器人能够自行完成人类主导的任务。
As much as this could be an aspect of data science, it is not all there is to data science. Rather, AI is part of the data science toolbox. Areas such as machine learning (ML) and AI have grown to become popular aspects of data science because they are incredibly powerful tools.
尽管这可能是数据科学的一个方面，但它并不是数据科学的全部。 相反，AI是数据科学工具箱的一部分。 机器学习(ML)和AI等领域已经成为数据科学的热门方面，因为它们是功能强大的工具。
These tools are powerful because they learn and adapt to optimize the outcome of a situation which they are tasked with. This is important because although humans can learn and adapt to optimize outcomes, machines currently have the upper hand at completing this on a larger scale.
这些工具之所以强大，是因为它们可以学习并适应以优化他们所负责的情况的结果。 这很重要，因为尽管人类可以学习并适应以优化结果，但机器目前在更大范围内完成此方面具有优势。
Many problems are quite complexed, and it would be unreasonable to ask a human to work through one such problem. Rather, humans should leverage their knowledge of a situation and combine this with both computing power and data to achieve substantial results. At the intersection of this is Deep Learning!
许多问题非常复杂，要求人们解决一个这样的问题是不合理的。 相反，人类应该利用他们对情况的了解，并将其与计算能力和数据相结合，以取得可观的结果。 在这一点上，是深度学习 ！
So, what exactly is deep learning? Broadly speaking, it is an application of AI. Since deep learning is a subset of AI, we must first understand AI and what it seeks to achieve. AI is any technique which enables a computer to mimic human behaviour. As the name suggests, it is a branch of computer science which emphasizes the development of intelligence within machines. In this case, intelligence can be considered to be the ability to process information which can be used to inform future decisions.
那么，深度学习到底是什么？ 从广义上讲，它是AI的应用。 由于深度学习是AI的子集，因此我们必须首先了解AI及其寻求实现的目标。 人工智能是任何能够使计算机模仿人类行为的技术。 顾名思义，它是计算机科学的一个分支，强调计算机内部智能的发展。 在这种情况下，可以将智能视为处理信息的能力，该信息可用于为将来的决策提供依据。
Therefore, the goal of AI is to develop efficient algorithms which can process information that can inform future decisions. ML is often used to achieve this goal. ML is a subset of AI and it provides systems with the ability to automatically learn and improve from experience without being explicitly programmed.
因此，AI的目标是开发可处理能够为将来的决策提供信息的高效算法。 ML通常用于实现此目标。 ML是AI的子集，它使系统能够自动学习并从经验中改进，而无需进行显式编程。
Now ‘learning’ has been mentioned a lot. However, machines are not reading books, conducting research nor asking questions to learn as humans do. Rather, ML algorithms use computational methods to understand information directly from data without relying on a predetermined equation as a model.
现在，“学习”已经被大量提及。 但是，机器无法像人类一样阅读书籍，进行研究或询问问题以学习。 而是，ML算法使用计算方法来直接从数据中了解信息，而无需依赖于预定的方程式作为模型。
To do this, the algorithms are made to determine a pattern in data and develop a target function which best maps an input variable, x, to a target variable, y. Deep learning is a subset which takes this idea further. The goal of deep learning is to automatically extract the most useful pieces of information needed to inform future decisions.
为此，需要使用算法来确定数据中的模式并开发出目标函数，该函数最好将输入变量x映射到目标变量y 。 深度学习是使这一思想更进一步的子集。 深度学习的目标是自动提取最有用的信息，以为将来的决策提供依据。
The idea of taking ML a step further can seem a bit abstract and thus, blurring the difference between ML and deep learning a bit. The idea is that typical ML algorithms attempt to define a set of rules within data, and these rules are usually hand-engineered. Because of this, ML algorithms can also be less ideal than expected when placed in outside of a development environment.
进一步推进ML的想法似乎有点抽象，因此模糊了ML和深度学习之间的差异。 这个想法是典型的ML算法尝试在数据中定义一组规则，这些规则通常是手工设计的。 因此，当放置在开发环境之外时，ML算法也可能不如预期的理想。
Consider ‘ThisIsPizza’, which is my fictional app to detect whether the object in a picture is a pizza slice or not. Now, having an app and an algorithm which can accurately determine whether an object is pizza is important because one would not want to eat a triangular object which looks like pizza but is not pizza. Pizza is very complexed, and recall that machines are better at dealing with complexed situations than humans are. Therefore, the classification rule could then be if there is a triangular object with at least tomato sauce, cheese, pepperoni, and a crust at the base of the triangle, then it is a pizza. But then, the obvious question would be, how to determine whether something is tomato sauce, cheese, and pepperoni?
考虑“ ThisIsPizza ”，这是我的虚构应用程序，用于检测图片中的对象是否为披萨片。 现在，拥有一款能够准确确定对象是否为比萨的应用程序和算法非常重要，因为人们不想吃看起来像比萨但不是比萨的三角形对象。 比萨非常复杂，回想起来，机器比人类更擅长处理复杂情况。 因此，分类规则可能是：如果存在一个三角形物体，且该物体至少具有番茄酱，奶酪，意大利辣香肠和三角形底面的外壳，则为披萨。 但是，显而易见的问题是，如何确定番茄酱，奶酪和意大利辣香肠是什么？
The idea of deep learning is that these features will be learnt just from raw data. There would be no need to define pepperoni as a red-ish circular image. And could you imagine defining cheese or tomato sauce? Rather, the deep learning model will develop a hierarchical representation of lines, curvature, and other features which can be used to distinguish cheese from tomato sauce, and then combine these features to then detect higher-level features such as pizza slices.
深度学习的思想是仅从原始数据中学习这些功能。 无需将意大利辣香肠定义为淡红色的圆形图像。 您能想象定义奶酪或番茄酱吗？ 而是，深度学习模型将开发线，曲率和其他特征的层次表示形式，这些特征可用于区分奶酪和番茄酱，然后将这些特征进行组合以检测更高层次的特征，例如比萨饼切片。
Although ThisIsPizza is being used as a toy example, the concepts explained are utilized in everyday applications to overcome multiple ‘real-world’ challenges. Understanding the challenges faces by ThisIsPizza, along with how deep learning can be used to overcome these challenges, is essential to understanding what exactly deep learning is.
尽管ThisIsPizza被用作玩具示例，但在日常应用中仍采用了所解释的概念来克服多个“现实世界”挑战。 了解ThisIsPizza面临的挑战，以及如何利用深度学习来克服这些挑战，对于理解什么是深度学习至关重要。
To use a more concrete example, let us consider Apple’s Face ID. This is a facial recognition system that can be used to unlock Apple devices, as well as securely act as an authentication system when using services such as Apple Pay. Without going into too much detail, the Face ID system uses powerful cameras to detect and map a user’s face. But how does the software know that what it sees is actually a face? Said differently, it would be a major problem if a user accidentally scanned their leg and payment then goes through.
要使用更具体的示例，让我们考虑Apple的Face ID。 这是一个面部识别系统，可用于解锁Apple设备，以及在使用Apple Pay等服务时安全地充当身份验证系统。 无需过多介绍细节，Face ID系统使用功能强大的摄像头来检测和绘制用户的面部。 但是软件如何知道所看到的实际上是一张脸？ 换句话说，如果用户不小心扫描了他们的腿，然后付款，这将是一个主要问题。
To determine whether something is a face, the algorithm might try to recognize a mouth, eyes, and nose. Once these are present, then the algorithm might classify the image as a face. But again, the question comes up about how to distinguish a mouth, eyes, and nose. So, to then distinguish these features further, we might say that a mouth is a pair of lines with a particular orientation and that these lines should not be situated above the nose. These rules can continuously become more complexed, and they would need to be created for every sub-item of interest.
为了确定某物是否是一张脸，该算法可能会尝试识别嘴，眼和鼻子。 一旦存在这些，则算法可以将图像分类为面部。 但是同样，关于如何区分嘴巴，眼睛和鼻子的问题又来了。 因此，为了进一步区分这些特征，我们可以说嘴是一对具有特定方向的线，并且这些线不应位于鼻子上方。 这些规则可能会变得越来越复杂，因此需要为每个感兴趣的子项目都创建它们。
Therefore, the key idea of deep learning is that these features need to be learned just from raw data. The algorithm would learn this by being fed thousands of images of faces, and it will then develop a hierarchical method of recognizing faces. First, it might try to detect low-level features like lines, edges, and corners. Next, it can use these to detect mid-level features such as mouths, eyes, and noses. Then, composing these together to detect high-level features such as facial hair or dimples.
因此，深度学习的关键思想是仅从原始数据中学习这些功能。 该算法将通过馈入数千张面部图像来学习此方法，然后将开发一种识别面部的分层方法。 首先，它可能会尝试检测线，边和角等低级特征。 接下来，它可以使用这些来检测中级特征，例如嘴，眼睛和鼻子。 然后，将它们组合在一起以检测高级特征，例如面部毛发或酒窝。
So, what exactly is deep learning? It is a powerful process which enables a computer to mimic human behaviour by automatically extracting the most useful pieces of information needed to inform future decisions. Data science is revolutionizing many fields and this is partly due to the advances in incredible tools such as AI and computing power. Real-world problems are quite complexed, so let’s try to solve them using deep learning!
那么，深度学习到底是什么？ 它是一个功能强大的过程，它使计算机能够通过自动提取最有用的信息来为将来的决策提供信息，从而模仿人类的行为。 数据科学正在革新许多领域，部分原因是由于AI和计算能力等令人难以置信的工具的发展。 现实世界中的问题非常复杂，因此让我们尝试使用深度学习来解决它们！
machinelearningmastery.com/what-is-deep-learning/
machinelearningmastery.com/what-is-deep-learning/
neuralnetworksanddeeplearning.com/
neuronetworksanddeeplearning.com/
mathworks.com/discovery/deep-learning.html
mathworks.com/discovery/deep-learning.html
Other Useful Material:
其他有用的材料：
towardsdatascience.com/what-is-deep-learning-and-how-does-it-work-2ce44bb692ac
directiondatascience.com/what-is-deep-learning-and-how-it-it-work-2ce44bb692ac
www.youtube.com/watch?v=6M5VXKLf4D4
www.youtube.com/watch?v=6M5VXKLf4D4
deeplearning.mit.edu/
deeplearning.mit.edu/
https://www.inertia7.com/tristn
https://www.inertia7.com/tristn
翻译自: https://towardsdatascience.com/what-is-deep-learning-adf5d4de9afc
深度学习中的深度是指什么