机器学习算法类别_机器学习-类别

机器学习算法类别

机器学习-类别 (Machine Learning - Categories)

监督学习 (Supervised Learning)

Supervised learning is analogous to training a child to walk. You will hold the child’s hand, show him how to take his foot forward, walk yourself for a demonstration and so on, until the child learns to walk on his own.

监督学习类似于训练孩子走路。您将握住孩子的手，向他展示如何使自己的脚向前走，自己示范自己，等等，直到孩子学会自己走路。

回归 (Regression)

Similarly, in the case of supervised learning, you give concrete known examples to the computer. You say that for given feature value x1 the output is y1, for x2 it is y2, for x3 it is y3, and so on. Based on this data, you let the computer figure out an empirical relationship between x and y.

同样，在监督学习的情况下，您可以给计算机提供具体的已知示例。您说对于给定的特征值x1，输出为y1，对于x2为y2，对于x3为y3，依此类推。根据这些数据，您可以让计算机找出x和y之间的经验关系。

Once the machine is trained in this way with a sufficient number of data points, now you would ask the machine to predict Y for a given X. Assuming that you know the real value of Y for this given X, you will be able to deduce whether the machine’s prediction is correct.

用足够数量的数据点以这种方式训练机器之后，现在您将要求机器为给定的X预测Y。假设您知道给定X的Y的实际值，则可以得出机器的预测是否正确。

Thus, you will test whether the machine has learned by using the known test data. Once you are satisfied that the machine is able to do the predictions with a desired level of accuracy (say 80 to 90%) you can stop further training the machine.

因此，您将使用已知的测试数据来测试机器是否已学习。一旦您对机器能够以所需的准确度(例如80％到90％)进行预测感到满意，就可以停止进一步训练机器。

Now, you can safely use the machine to do the predictions on unknown data points, or ask the machine to predict Y for a given X for which you do not know the real value of Y. This training comes under the regression that we talked about earlier.

现在，您可以安全地使用机器对未知数据点进行预测，或要求机器针对您不知道Y实际值的给定X预测Y。此训练来自我们所讨论的回归较早。

分类 (Classification)

You may also use machine learning techniques for classification problems. In classification problems, you classify objects of similar nature into a single group. For example, in a set of 100 students say, you may like to group them into three groups based on their heights - short, medium and long. Measuring the height of each student, you will place them in a proper group.

您也可以使用机器学习技术来解决分类问题。在分类问题中，您将相似性质的对象分类为一个组。例如，假设有一组100名学生说，您可能希望根据身高将他们分为三类-矮，中和长。测量每个学生的身高，您可以将他们放在适当的组中。

Now, when a new student comes in, you will put him in an appropriate group by measuring his height. By following the principles in regression training, you will train the machine to classify a student based on his feature – the height. When the machine learns how the groups are formed, it will be able to classify any unknown new student correctly. Once again, you would use the test data to verify that the machine has learned your technique of classification before putting the developed model in production.

现在，当一个新学生进来时，您可以通过测量他的身高将他分成合适的组。通过遵循回归训练中的原理，您将训练机器以根据学生的特征(身高)对学生进行分类。当机器学习如何形成组时，它将能够正确地对任何未知的新学生进行分类。再一次，您将使用测试数据来验证机器在将开发的模型投入生产之前已经学习了您的分类技术。

Supervised Learning is where the AI really began its journey. This technique was applied successfully in several cases. You have used this model while doing the hand-written recognition on your machine. Several algorithms have been developed for supervised learning. You will learn about them in the following chapters.

监督学习是AI真正开始其旅程的地方。该技术已成功应用于多种情况。您在机器上进行手写识别时使用了此模型。已经开发了几种用于监督学习的算法。您将在以下各章中了解它们。

无监督学习 (Unsupervised Learning)

In unsupervised learning, we do not specify a target variable to the machine, rather we ask machine “What can you tell me about X?”. More specifically, we may ask questions such as given a huge data set X, “What are the five best groups we can make out of X?” or “What features occur together most frequently in X?”. To arrive at the answers to such questions, you can understand that the number of data points that the machine would require to deduce a strategy would be very large. In case of supervised learning, the machine can be trained with even about few thousands of data points. However, in case of unsupervised learning, the number of data points that is reasonably accepted for learning starts in a few millions. These days, the data is generally abundantly available. The data ideally requires curating. However, the amount of data that is continuously flowing in a social area network, in most cases data curation is an impossible task.

在无监督学习中，我们没有为机器指定目标变量，而是询问机器“您能告诉我有关X的什么信息？”。更具体地说，我们可能会问一些问题，例如给定一个庞大的数据集X，“我们可以从X得出的五个最佳分组是什么？” 或“哪些功能在X中最常同时出现？”。为了得到这些问题的答案，您可以理解，机器推导出策略所需的数据点数量将非常大。在监督学习的情况下，甚至可以用数千个数据点来训练机器。但是，在无监督学习的情况下，为学习而合理接受的数据点的数量从几百万开始。这些天，数据通常非常丰富。理想情况下，数据需要整理。但是，在大多数情况下，社交区域网络中不断流动的数据量是一项不可能的任务。

The following figure shows the boundary between the yellow and red dots as determined by unsupervised machine learning. You can see it clearly that the machine would be able to determine the class of each of the black dots with a fairly good accuracy.

下图显示了由无监督机器学习确定的黄点和红点之间的边界。您可以清楚地看到，该机器将能够以相当好的精度确定每个黑点的类别。

Source:

资源：

https://chrisjmccormick.files.wordpress.com/2013/08/approx_decision_boun dary.png

The unsupervised learning has shown a great success in many modern AI applications, such as face detection, object detection, and so on.

无监督学习已在许多现代AI应用程序中取得了巨大的成功，例如人脸检测，对象检测等。

强化学习 (Reinforcement Learning)

Consider training a pet dog, we train our pet to bring a ball to us. We throw the ball at a certain distance and ask the dog to fetch it back to us. Every time the dog does this right, we reward the dog. Slowly, the dog learns that doing the job rightly gives him a reward and then the dog starts doing the job right way every time in future. Exactly, this concept is applied in “Reinforcement” type of learning. The technique was initially developed for machines to play games. The machine is given an algorithm to analyze all possible moves at each stage of the game. The machine may select one of the moves at random. If the move is right, the machine is rewarded, otherwise it may be penalized. Slowly, the machine will start differentiating between right and wrong moves and after several iterations would learn to solve the game puzzle with a better accuracy. The accuracy of winning the game would improve as the machine plays more and more games.

考虑训练宠物狗，我们训练宠物将球带给我们。我们将球扔到一定距离，然后请狗把它取回给我们。每当狗做这件事时，我们都会奖励狗。慢慢地，狗知道正确地做这项工作会给他奖励，然后狗以后每次都会以正确的方式开始做这项工作。确实，此概念适用于“强化”类型的学习。该技术最初是为玩游戏的机器开发的。为机器提供了一种算法，可以分析游戏每个阶段的所有可能动作。机器可以随机选择其中一个动作。如果移动正确，则将奖励该机器，否则可能会受到处罚。慢慢地，机器将开始区分对与错步，经过多次迭代将学会更好地解决游戏难题。随着机器玩越来越多的游戏，赢得比赛的准确性将会提高。

The entire process may be depicted in the following diagram −

下图描述了整个过程-

This technique of machine learning differs from the supervised learning in that you need not supply the labelled input/output pairs. The focus is on finding the balance between exploring the new solutions versus exploiting the learned solutions.

这种机器学习技术与监督学习的不同之处在于，您无需提供标记的输入/输出对。重点是在探索新解决方案与利用学习到的解决方案之间找到平衡。

深度学习 (Deep Learning)

The deep learning is a model based on Artificial Neural Networks (ANN), more specifically Convolutional Neural Networks (CNN)s. There are several architectures used in deep learning such as deep neural networks, deep belief networks, recurrent neural networks, and convolutional neural networks.

深度学习是基于人工神经网络(ANN)，更具体地说是卷积神经网络(CNN)的模型。深度学习中使用了多种架构，例如深度神经网络，深度置信网络，递归神经网络和卷积神经网络。

These networks have been successfully applied in solving the problems of computer vision, speech recognition, natural language processing, bioinformatics, drug design, medical image analysis, and games. There are several other fields in which deep learning is proactively applied. The deep learning requires huge processing power and humongous data, which is generally easily available these days.

这些网络已成功应用于解决计算机视觉，语音识别，自然语言处理，生物信息学，药物设计，医学图像分析和游戏等问题。积极应用深度学习还有其他几个领域。深度学习需要强大的处理能力和庞大的数据，这些天通常很容易获得。

We will talk about deep learning more in detail in the coming chapters.

在接下来的章节中，我们将更详细地讨论深度学习。

深度强化学习 (Deep Reinforcement Learning)

The Deep Reinforcement Learning (DRL) combines the techniques of both deep and reinforcement learning. The reinforcement learning algorithms like Q-learning are now combined with deep learning to create a powerful DRL model. The technique has been with a great success in the fields of robotics, video games, finance and healthcare. Many previously unsolvable problems are now solved by creating DRL models. There is lots of research going on in this area and this is very actively pursued by the industries.

深度强化学习(DRL)结合了深度学习和强化学习的技术。现在，像Q学习这样的强化学习算法与深度学习相结合，以创建强大的DRL模型。该技术在机器人技术，视频游戏，金融和医疗保健领域取得了巨大的成功。现在，通过创建DRL模型可以解决许多以前无法解决的问题。在这个领域有很多研究正在进行，并且行业非常积极地追求这一点。

So far, you have got a brief introduction to various machine learning models, now let us explore slightly deeper into various algorithms that are available under these models.

到目前为止，您已经对各种机器学习模型进行了简要介绍，现在让我们更深入地探索这些模型下可用的各种算法。

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