机器学习深度学习加强学习

机器学习-深度学习 (Machine Learning - Deep Learning)

Deep Learning uses ANN. First we will look at a few deep learning applications that will give you an idea of its power.

深度学习使用ANN。 首先，我们将研究一些深度学习应用程序，这些应用程序将使您对其功能有所了解。

应用领域 (Applications)

Deep Learning has shown a lot of success in several areas of machine learning applications.

深度学习在机器学习应用程序的多个领域显示出了许多成功。

Self-driving Cars − The autonomous self-driving cars use deep learning techniques. They generally adapt to the ever changing traffic situations and get better and better at driving over a period of time.

自动驾驶汽车 -自动驾驶汽车使用深度学习技术。 它们通常会适应不断变化的交通状况，并且在一段时间内会越来越好。

Speech Recognition − Another interesting application of Deep Learning is speech recognition. All of us use several mobile apps today that are capable of recognizing our speech. Apple’s Siri, Amazon’s Alexa, Microsoft’s Cortena and Google’s Assistant – all these use deep learning techniques.

语音识别 -深度学习的另一个有趣的应用是语音识别。 今天我们所有人都使用能够识别我们语音的几个移动应用程序。 苹果的Siri，亚马逊的Alexa，微软的Cortena和Google的助手-所有这些都使用深度学习技术。

Mobile Apps − We use several web-based and mobile apps for organizing our photos. Face detection, face ID, face tagging, identifying objects in an image – all these use deep learning.

移动应用程序 -我们使用多个基于Web的移动应用程序来组织照片。 人脸检测，人脸ID，人脸标记，识别图像中的对象–所有这些都使用深度学习。

深度学习的未开发机会 (Untapped Opportunities of Deep Learning)

After looking at the great success deep learning applications have achieved in many domains, people started exploring other domains where machine learning was not so far applied. There are several domains in which deep learning techniques are successfully applied and there are many other domains which can be exploited. Some of these are discussed here.

在研究了深度学习应用在许多领域取得的巨大成功之后，人们开始探索到目前为止尚未应用机器学习的其他领域。 深度学习技术在多个领域得到成功应用，其他许多领域也可以被利用。 其中一些在这里讨论。

• Agriculture is one such industry where people can apply deep learning techniques to improve the crop yield.

  农业就是这样一种行业，人们可以应用深度学习技术来提高农作物的产量。

• Consumer finance is another area where machine learning can greatly help in providing early detection on frauds and analyzing customer’s ability to pay.

  消费金融是机器学习可以极大地帮助提供欺诈的早期检测并分析客户的支付能力的另一个领域。

• Deep learning techniques are also applied to the field of medicine to create new drugs and provide a personalized prescription to a patient.

  深度学习技术还应用于医学领域，以开发新药并向患者提供个性化处方。

The possibilities are endless and one has to keep watching as the new ideas and developments pop up frequently.

可能性是无止境的，随着新思想和新发展的频繁出现，人们必须不断观察。

使用深度学习实现更多要求 (What is Required for Achieving More Using Deep Learning)

To use deep learning, supercomputing power is a mandatory requirement. You need both memory as well as the CPU to develop deep learning models. Fortunately, today we have an easy availability of HPC – High Performance Computing. Due to this, the development of the deep learning applications that we mentioned above became a reality today and in the future too we can see the applications in those untapped areas that we discussed earlier.

要使用深度学习，超级计算能力是强制性要求。 您需要内存和CPU来开发深度学习模型。 幸运的是，今天我们可以轻松获得高性能计算(HPC)。 因此，上面提到的深度学习应用程序的开发在今天已经成为现实，在将来，我们也可以在我们前面讨论的那些尚未开发的领域中看到这些应用程序。

Now, we will look at some of the limitations of deep learning that we must consider before using it in our machine learning application.

现在，我们将研究在机器学习应用程序中使用深度学习之前必须考虑的一些局限性。

深度学习的缺点 (Deep Learning Disadvantages)

Some of the important points that you need to consider before using deep learning are listed below −

下面列出了在使用深度学习之前需要考虑的一些重要点-

• Black Box approach
  黑匣子方法
• Duration of Development
  开发时间
• Amount of Data
  数据量
• Computationally Expensive
  计算昂贵

We will now study each one of these limitations in detail.

现在，我们将详细研究这些限制中的每一个。

黑匣子方法 (Black Box approach)

An ANN is like a blackbox. You give it a certain input and it will provide you a specific output. The following diagram shows you one such application where you feed an animal image to a neural network and it tells you that the image is of a dog.

一个人工神经网络就像一个黑匣子。 您给它一个特定的输入，它将为您提供一个特定的输出。 下图显示了一个这样的应用程序，在该应用程序中，您将动物图像输入神经网络，并告诉您该图像是狗的。

Why this is called a black-box approach is that you do not know why the network came up with a certain result. You do not know how the network concluded that it is a dog? Now consider a banking application where the bank wants to decide the creditworthiness of a client. The network will definitely provide you an answer to this question. However, will you be able to justify it to a client? Banks need to explain it to their customers why the loan is not sanctioned?

为什么将其称为黑盒方法，是因为您不知道为什么网络得出了一定的结果。 您不知道网络如何断定那是狗吗？ 现在考虑银行想要确定客户信誉的银行应用程序。 该网络肯定会为您提供该问题的答案。 但是，您可以向客户证明它的合理性吗？ 银行需要向客户解释为什么不批准贷款？

开发时间 (Duration of Development)

The process of training a neural network is depicted in the diagram below −

下图描述了训练神经网络的过程-

You first define the problem that you want to solve, create a specification for it, decide on the input features, design a network, deploy it and test the output. If the output is not as expected, take this as a feedback to restructure your network. This is an iterative process and may require several iterations until the time network is fully trained to produce desired outputs.

首先，您要定义要解决的问题，为其创建一个规范，确定输入功能，设计网络，进行部署并测试输出。 如果输出与预期不符，请以此作为重组网络的反馈。 这是一个反复的过程，可能需要多次迭代，直到时间网络经过充分训练以产生所需的输出为止。

数据量 (Amount of Data)

The deep learning networks usually require a huge amount of data for training, while the traditional machine learning algorithms can be used with a great success even with just a few thousands of data points. Fortunately, the data abundance is growing at 40% per year and CPU processing power is growing at 20% per year as seen in the diagram given below −

深度学习网络通常需要大量的数据来进行训练，而即使只有数千个数据点，传统的机器学习算法也可以非常成功地使用。 幸运的是，如下图所示，数据量以每年40％的速度增长，CPU处理能力以每年20％的速度增长-

计算昂贵 (Computationally Expensive)

Training a neural network requires several times more computational power than the one required in running traditional algorithms. Successful training of deep Neural Networks may require several weeks of training time.

训练神经网络所需的计算能力是运行传统算法所需的计算能力的几倍。 深度神经网络的成功培训可能需要数周的培训时间。

In contrast to this, traditional machine learning algorithms take only a few minutes/hours to train. Also, the amount of computational power needed for training deep neural network heavily depends on the size of your data and how deep and complex the network is?

与此相反，传统的机器学习算法只需要花费几分钟/几小时即可进行训练。 此外，训练深度神经网络所需的计算能力在很大程度上取决于数据的大小以及网络的深度和复杂程度？

After having an overview of what Machine Learning is, its capabilities, limitations, and applications, let us now dive into learning “Machine Learning”.

在概述了什么是机器学习，其功能，局限性和应用之后，让我们现在开始学习“机器学习”。

翻译自: https://www.tutorialspoint.com/machine_learning/deep_machine_learning.htm

机器学习深度学习加强学习