Artificial intelligence is any technique that enables machines — computers, in particular — to mimic human behaviour and perform similar tasks. Most software could fall under this broad definition. Ultimately, the software intermediates as an agent between us and our objectives, namely to buy online, register a warehouse movement, or study. If such software does not exist, another human agent should step forward to replace it. Then we should instead meet a commercial agent, a logistics manager, or a teacher of the desired subject.
人工智能是使机器(尤其是计算机)能够模仿人类行为并执行类似任务的任何技术。 大多数软件都可以属于这个广义的定义。 最终，该软件将中介作为我们与目标之间的中介，即在线购买，注册仓库搬迁或学习。 如果不存在此类软件，则应由其他人工代理替换。 然后，我们应该代替商业代理商，物流经理或所需学科的老师。
With this definition in mind, let’s first clarify what machine learning is.
牢记这个定义，让我们首先阐明什么是机器学习。
What is machine learning?
什么是机器学习？
The first definitions of machine learning included a differentiating element with respect to our first definition of artificial intelligence: the improvement of the system through experience. That kind of software should be able to improve your performance on a task through experience performing that task in an iterative process.
机器学习的第一个定义相对于我们对人工智能的第一个定义包含了一个与众不同的元素：通过经验改进系统。 这类软件应该能够通过在迭代过程中执行任务的经验来提高您在任务上的性能。
The problems that artificial intelligence systems solve are limited to two main types: classification problems, in which we try to predict discrete responses, for example, a grouping in families — clustering — of data in disjoint sets; or regression problems, where we predict continuous responses, for example calculating the optimal value for a certain action, which can be included in a continuous range of values.
人工智能系统解决的问题仅限于两种主要类型：分类问题，在分类问题中，我们尝试预测离散的响应，例如，不连续集合中的数据按家庭分组-聚类； 或回归问题，我们可以预测连续响应，例如计算某个动作的最佳值，该值可以包含在连续的值范围内。
The first definitions of machine learning included a differentiating element with respect to our first definition of artificial intelligence: the improvement of the system through experience. That kind of software should be able to improve your performance on a task through experience performing that task in an iterative process.
机器学习的第一个定义相对于我们对人工智能的第一个定义包含了一个与众不同的元素：通过经验改进系统。 这类软件应该能够通过在迭代过程中执行任务的经验来提高您在任务上的性能。
The problems that artificial intelligence systems solve are limited to two main types: classification problems, in which we try to predict discrete responses, for example, a grouping in families — clustering — of data in disjoint sets; or regression problems, where we predict continuous responses, for example calculating the optimal value for a certain action, which can be included in a continuous range of values.
人工智能系统解决的问题仅限于两种主要类型：分类问题，在分类问题中，我们尝试预测离散的响应，例如，不连续集合中的数据按家庭分组-聚类； 或回归问题，我们可以预测连续响应，例如计算某个动作的最佳值，该值可以包含在连续的值范围内。
The ways in which we humans teach machines to “learn through experience” can be summarized in three: supervised learning; unsupervised learning; and reinforcement learning.
我们人类教机器“通过经验学习”的方式可以概括为三种：监督学习； 无监督学习； 和强化学习。
In supervised learning, we humans teach machines what should they know. For example, if we are tackling an animal classification problem, we will label images as lions or elephants. In unsupervised learning, no human intervention occurs in the data processing. One notable example was Microsoft’s famous twitter bot Tay, which had to be unplugged after just 24 hours of operation for his misogynistic and ideological comments. We typically use it to discover underlying structures that we do not know about in our data sets. That is, in that case, we use machines to learn from their conclusions. Reinforcement learning is something similar to what we do when we train our pets: when they perform as expected, the algorithms receive a reward.
在监督学习中，我们人类教机器他们应该知道什么。 例如，如果要解决动物分类问题，则将图像标记为狮子或大象。 在无监督学习中，在数据处理中不会发生人为干预。 一个著名的例子是微软著名的推特机器人Tay，由于他的厌恶和意识形态的评论，在操作仅24小时后就不得不拔掉电源。 我们通常使用它来发现我们在数据集中不了解的基础结构。 也就是说，在这种情况下，我们使用机器从他们的结论中学习。 强化学习类似于我们训练宠物时所做的事情：当宠物表现出预期时，算法会获得奖励。
Note that the problems mentioned above can be tackled with all three techniques, in particular, the two antagonistic ones: supervised or unsupervised — since reinforcement in the background is a form of supervision. When we attack classification problems with supervised learning, we usually want the machine to learn to distinguish things that we already know, with the aim that it later does it for us, for example, artificial vision systems that recognize objects in a warehouse. It is possible to see real cases of very precise image recognition — textures, shapes, objects — on the IBM Watson website (https://visual-recognition-code-pattern.ng.bluemix.net/) or using apps like Vivino that allows us to know about a bottle of wine simply by taking a picture of its label. In the case of unsupervised learning, what we want is to discover structures that we may be missing. When we talk about continuous problems, with supervised learning we will be able to teach the machine a linear adjustment or formula so that from certain variables it is able to predict others — hence, for example, its use to find price optimals. Unsupervised learning in the continuous domain is often used for the so-called dimensionality reduction — that is, to teach us to display data in a simplified way. For example, cleaning variables from a model that has no correlation with what we are looking for.
请注意，上述问题可以通过所有三种技术来解决，特别是两种对立技术：有监督或无监督的-因为在后台进行强化是一种监督形式。 当我们通过监督学习来攻击分类问题时，我们通常希望机器学习以区分我们已经知道的事物，以期以后为我们完成它的目标，例如识别仓库中物体的人工视觉系统。 在IBM Watson网站( https://visual-recognition-code-pattern.ng.bluemix.net/ )或使用诸如Vivino之类的应用程序，可以看到非常精确的图像识别的真实情况-纹理，形状，对象。让我们仅通过拍照就可以了解一瓶葡萄酒。 在无监督学习的情况下，我们想要的是发现我们可能缺少的结构。 当我们谈论持续的问题时，通过监督学习，我们将可以教机器一个线性调整或公式，以便从某些变量中可以预测其他变量-因此，例如，它可以用来寻找最优价格。 连续域中的无监督学习通常用于所谓的降维-也就是说，教我们以简化的方式显示数据。 例如，从模型中清除与我们要查找的变量不相关的变量。
Other notable day-to-day examples where machine learning techniques are applied are recommender algorithms — such as those of Netflix or Amazon — or real-time prediction systems of the fastest route based on existing traffic, as occurs in the Waze and Google Maps apps.
应用机器学习技术的其他值得注意的日常示例是推荐算法(例如Netflix或Amazon的算法)或基于现有流量的最快路线的实时预测系统，如Waze和Google Maps应用中的情况。
So what is the difference with Deep Leaning?
那么深度学习有什么区别？
Well, so far, everything we have talked about has been related to machine learning. What then is deep learning or deep learning (DL)? DL is just a technical subset of machine learning. A particular way of doing machine learning, using connectionist systems or neural networks. The most useful notion to begin to understand the difference is knowing that deep learning is machine learning.
好吧，到目前为止，我们所讨论的一切都与机器学习有关。 那么深度学习或深度学习(DL)是什么？ DL只是机器学习的技术子集。 使用连接系统或神经网络进行机器学习的一种特殊方式。 开始了解差异的最有用的概念是知道深度学习是机器学习。
The differences between ML and DL, therefore, do not lie in their use or application, which are the same as those mentioned previously, but in technical issues, such as data consumption, the need for human pre-processing of data — feature engineering — , and, in general, the complexity of the problems dealt with, being DL the most used technique to attack larger data sets.
因此，ML和DL之间的区别不在于它们的使用或应用(与前面提到的相同)，而在于技术问题，例如数据消耗，对数据进行人工预处理的需要-功能工程- ，并且通常来说，问题的复杂性是DL攻击大型数据集的最常用技术。