深度学习和机器学习之间的区别

小咪咪 2022-12-07 13:27 263阅读 0赞

Here you will learn about difference between deep learning and machine learning.

在这里，您将了解深度学习和机器学习之间的区别。

We already are aware of the term and in brief that [Deep Learning][] is the subset of a wider domain called [Machine Learning][]. If talking combined of Machine Learning and Deep Learning we can think of how Netflix is able to predict and recommend shows to watch based on your taste and how Facebook is able to recognize the face in the pictures you upload.

简而言之，我们已经知道该术语， [深度学习][Deep Learning]是称为[机器学习][Machine Learning]的更广泛领域的子集。 如果将机器学习和深度学习相结合，我们可以考虑Netflix如何根据您的喜好来预测和推荐要观看的节目，以及Facebook如何在您上传的图片中识别人脸。

If you’re interested to learn Machine learning and also want to become a [Master in Data Science][] then Intellipaat’s Data science and [Machine learning Course][] is for you.

如果您有兴趣学习机器学习并且还想成为[数据科学硕士，][Master in Data Science]那么Intellipaat的数据科学和[机器学习课程][Machine learning Course]非常适合您。

Also as Machine Learning is the superset of Deep Learning, [Artificial Intelligence][] is the superset of Machine Learning. So, instead of using these terms interchangeably we should be able to distinguish between them.

就像机器学习是深度学习的超集一样， [人工智能][Artificial Intelligence]也是机器学习的超集。 因此，我们应该能够区分它们，而不是交替使用这些术语。

Deep Learning is being used by Google in its image and voice recognition algorithms, by Amazon to predict and recommend what a customer wants next and by MIT researchers to predict the future*.*

Google在其图像和语音识别算法中使用了深度学习，亚马逊在预测和推荐客户下一步需求时使用了深度学习，麻省理工学院的研究人员正在使用它们来预测未来*。*

Before moving further let us quickly get a brief intro of what Deep Learning actually do so as to maintain its existence. It is quite clear that it will be focusing on the principles of ML and AI being subset of them. So come let’s see what’s new there for us to learn.

在进一步介绍之前，让我们快速简要介绍一下深度学习实际上是做什么的，以保持其存在。 很明显，它将重点关注ML和AI的原理，而ML和AI是它们的子集。 那么，让我们来看看有什么新鲜事物可供我们学习。

### 深度学习如何工作？ **(**How does Deep Learning work?**)** ###

Primarily the basic concept behind Deep learning is to feed the computer with decent amount of data/information which can be later used for the decision making process about some other set of data. Now you might be wondering, how the heck are we going to feed the computer? Is that similar to the process we adopted in case of ML? Yes, you got this right. The exact same method as ML is also entertained in deep learning i.e. via [Neural Networks][]*.*

深度学习背后的基本概念主要是为计算机提供大量的数据/信息，这些数据/信息可稍后用于其他一些数据集的决策过程。 现在您可能想知道，我们将如何喂计算机？ 这类似于我们在ML情况下采用的过程吗？ 是的，您说对了。 在深度学习中，即通过[神经网络，][Neural Networks]也可以采用与ML完全相同的方法*。*

These networks are also termed as logical constructions which classify every bit of data that passes from them on the basis of answers received to every binary (TRUE/FALSE) questions being asked to the bits passing via network. Since Deep Learning is associated with the perspective of developing these networks, therefore are also known as Deep Neural Networks. Such networks are witnessed to process comparatively large datasets like Google’s image library, or Facebook’s feeds repository. Now we can very easily get an idea of what efforts are needed by computers to handle such a large datasets with extremely sophisticated networks. On the other hand how all these tasks are accomplished by humans with intense ease is remarkable.

这些网络也被称为逻辑结构，该逻辑结构根据对通过网络传递的位的每个二进制(TRUE / FALSE)问题所收到的答案，对从它们传递来的数据的每个位进行分类。 由于深度学习与开发这些网络的观点相关联，因此也被称为深度神经网络。 可以证明，此类网络可以处理相对较大的数据集，例如Google的图片库或Facebook的供稿存储库。 现在，我们可以很容易地了解计算机需要付出什么努力来处理具有极其复杂的网络的大型数据集。 另一方面，人类如何轻松地完成所有这些任务是很显着的。

Working of deep neural networks are better tested with images as inputs because of the fact that images consist of several different elements and it is pretty interesting to observe that how computer with its calculation-oriented, one track mind can learn to identify and distinguish the images like we humans do.

由于图像由几个不同的元素组成，因此可以更好地测试深度神经网络的工作，因为图像由几个不同的元素组成，而且很有趣地观察到计算机如何以其以计算为导向的跟踪思维可以学会识别和区分图像就像人类一样

**Note:** Deep Learning can also be applied on several other types of data such as signals, speech, audio, video, written texts, etc. to produce conclusions.

**注意：**深度学习还可以应用于其他几种类型的数据，例如信号，语音，音频，视频，书面文本等，以得出结论。

Let us make this explanation bit easy to understand with the help of an example.

让我们借助一个示例使这一解释容易理解。

**Problem Statement:** To take input of all the cars passing along a public road and classify them on the basis of make and model.

**问题陈述：**输入所有通过公共道路的汽车，并根据品牌和型号对其进行分类。

**Solution:** First step towards the solution would be to provide the system access to the large database containing the information about the cars (like shape, size, engine sound, etc.). This can be accomplished manually or in the most advanced manner where the system can be programmed to search the internet for the relevant information and interpret the information found there.

**解决方案：解决方案的**第一步是使系统能够访问大型数据库，该数据库包含有关汽车的信息(例如形状，尺寸，引擎声音等)。 这可以手动完成，也可以通过最高级的方式完成，其中可以对系统进行编程，以在Internet上搜索相关信息并解释在那里找到的信息。

Next step would be the intake of the data that needs to be processed. In this case the images and sound captured by cameras, microphones and other sensors are input to the system. The data from the sensors are compared with the data already being present within the system or the data what system has learned. And thus the system is able to classify the cars on the basis of their make and model with certain probability of accuracy.

下一步将是需要处理的数据的获取。 在这种情况下，摄像机，麦克风和其他传感器捕获的图像和声音将输入到系统中。 将来自传感器的数据与系统中已经存在的数据或系统已获悉的数据进行比较。 因此，该系统能够以一定的准确度根据汽车的制造商和型号对汽车进行分类。

Up till now this all was pretty straightforward. Now, the interesting part comes in when we talk about “Deep Learning” in this, as the time passes, the system gains more and more experience and become more able to classify the cars after being trained on new data with improved probability every time, like humans do. The system also learns from the mistakes that it make during the classification process just like humans do and with passing time the accuracy is observed to be improved significantly.

到目前为止，这一切都非常简单。 现在，当我们谈论“深度学习”时，有趣的部分就来了，随着时间的流逝，系统在接受新数据的训练后每次获得改进的可能性后，系统会获得越来越多的经验，并且能够对汽车进行分类，像人类一样。 该系统还从分类过程中犯的错误中学习，就像在人类中所做的一样，随着时间的流逝，观察到的准确性得到了显着提高。

Some of the noteworthy work and examples of Deep Learning are self-driving cars, predicting the outcome of legal proceedings, precision medicine, game playing and many more.

深度学习的一些值得注意的工作和示例是自动驾驶汽车，可预测法律诉讼，精准医学，游戏等方面的结果。

**Note:** In order to dive deeper in context of deep learning you may refer to Bernard Marr’s new book [Data Strategy][].

**注意：**为了更深入地学习深度学习，您可以参考Bernard Marr的新书[Data Strategy][] 。

## 深度学习和机器学习之间的区别 **(**Difference between Deep Learning and Machine Learning**)** ##

![Difference between Deep Learning and Machine Learning][]

[Image Source][]

[图片来源][Image Source]

As already told in the beginning of this post, Deep Learning is the subset of Machine Learning. A machine learning model needs to be told explicitly by feeding more and more data that how it should be making accurate prediction, on the contrary the deep learning model is capable of self-learning through its own method of computing (so-called its own brain).

如本文开头所述，深度学习是机器学习的子集。 需要通过提供越来越多的数据来明确告知机器学习模型应该如何进行准确的预测，相反，深度学习模型能够通过自己的计算方法(即所谓的自己的大脑)进行自学习)。

A deep learning model is designed in a way so as to interpret the data with some logic structure to copy the human’s ability of drawing conclusions. To accomplish this with simplicity and ease deep learning models uses a layered structure of algorithms known as *Artificial Neural Network* (ANN), whose structure is known to be very similar to the biological neural network present in human beings. Due to these facts the models made following the principles of deep learning are observed to be far more capable in the decision making process when compared to a typical machine learning model.

深度学习模型的设计方式是使用某种逻辑结构来解释数据，以复制人类得出结论的能力。 为了简单而轻松地完成此任务，深度学习模型使用称为*人工神经网络* (ANN)的算法的分层结构，该结构的结构与人类中存在的生物神经网络非常相似。 由于这些事实，与典型的机器学习模型相比，遵循深度学习原理的模型被认为在决策过程中具有更强大的功能。

Despite of all complex networks resembling a human neuron, it is not always ensured that models pertaining to deep learning will not draw an incorrect conclusion. Also observing the degree of certainty the deep learning models are considered as the potential support to AI. One of the noteworthy accomplishment in the field of deep learning is Google’s AlphaGo. Google created a deep learning model that become expert in a board game “Go” by playing against professional Go players and learning step by step what moves to make. The model was too featured in the news when it defeated multiple times winner.

尽管所有复杂的网络都类似于人类的神经元，但并不总是确保与深度学习有关的模型不会得出错误的结论。 同样观察到确定性的程度，深度学习模型被认为是对AI的潜在支持。 深度学习领域值得关注的成就之一是Google的AlphaGo。 Google创建了一种深度学习模型，通过与专业围棋玩家进行对抗并逐步学习如何做，从而成为棋盘游戏“围棋”中的专家。 当该模型多次击败获胜者时，它也成为新闻中的焦点。

In the last we think we should have a quick recap of what we have learnt so far in this post. To get a better grasp, let us do this in a tabular way.

最后，我们认为我们应该快速回顾一下本文到目前为止所学的内容。 为了更好地掌握，让我们以表格的方式进行。

<table> 
 <tbody> 
  <tr> 
   <td><strong>Machine Learning</strong></td> 
   <td><strong>Deep Learning</strong></td> 
  </tr> 
  <tr> 
   <td>Machine Learning is a subset of AI</td> 
   <td>Deep Learning is a subset of ML</td> 
  </tr> 
  <tr> 
   <td>Such models uses data to learn from them and then make decisions accordingly.</td> 
   <td>These models are capable of making any decision on their own.</td> 
  </tr> 
  <tr> 
   <td>Established algorithms are the basis to ML.</td> 
   <td>Artificial Neural Networks (ANN) forms the foundation of deep learning.</td> 
  </tr> 
  <tr> 
   <td>AI is the superset.</td> 
   <td>AI again is the superset.</td> 
  </tr> 
  <tr> 
   <td>ML models have set some great examples like weather forecasting.</td> 
   <td>Deep learning has got ML covered by providing even better capabilities to the existing ML models.</td> 
  </tr> 
  <tr> 
   <td>Examples: face recognition, spam filtering, weather forecasting, etc.</td> 
   <td>Examples: Google’s AlphaGo, Tesla’s self-driving car, etc.</td> 
  </tr> 
 </tbody> 
</table>

<table> 
 <tbody> 
  <tr> 
   <td> <strong>机器学习</strong> </td> 
   <td> <strong>深度学习</strong> </td> 
  </tr> 
  <tr> 
   <td> 机器学习是AI的子集 </td> 
   <td> 深度学习是ML的子集 </td> 
  </tr> 
  <tr> 
   <td> 这样的模型使用数据向他们学习，然后做出相应的决策。 </td> 
   <td> 这些模型能够自行做出任何决定。 </td> 
  </tr> 
  <tr> 
   <td> 既定的算法是机器学习的基础。 </td> 
   <td> 人工神经网络(ANN)构成了深度学习的基础。 </td> 
  </tr> 
  <tr> 
   <td> AI是超集。 </td> 
   <td> AI再次是超集。 </td> 
  </tr> 
  <tr> 
   <td> 机器学习模型已经树立了一些很好的例子，例如天气预报。 </td> 
   <td> 通过为现有ML模型提供甚至更好的功能，深度学习已涵盖了ML。 </td> 
  </tr> 
  <tr> 
   <td> 例如：人脸识别，垃圾邮件过滤，天气预报等。 </td> 
   <td> 例如：谷歌的AlphaGo，特斯拉的自动驾驶汽车等。 </td> 
  </tr> 
 </tbody> 
</table>

**End Notes**

**尾注**

We are pretty sure that our readers must be fascinated with the facts and figures we’ve discussed in this post regarding deep learning. Moreover as we have seen that a huge amount of data is required to fuel up the deep learning vehicle, this domain is believed to prosper in this era of big data and in times to come. It is believed that in the coming decades, deep learning would be having examples that humans cannot even imagine of.

我们非常确定，我们的读者一定会对我们在这篇文章中讨论的有关深度学习的事实和数据着迷。 此外，正如我们已经看到的那样，需要大量数据来加深深度学习工具的力量，在当今的大数据时代以及以后的时代，这一领域被认为是繁荣的。 人们相信，在未来的几十年中，深度学习将拥有人类甚至无法想象的例子。

In an interview with Wired Magazine, Baidu’s chief scientist *Andrew Ng* was reported to say : “*I think AI is akin to building a rocket ship. You need a huge engine and a lot of fuel, if you have large engine and a tiny amount of fuel, you can’t even lift off. To build a rocket you need a huge engine and a lot of fuel.*” (Source: [Wired][])

在接受《连线》杂志采访时，百度首席科学家*安德鲁·伍* ( *Andrew Ng)*被报道说：“ *我认为AI类似于建造火箭飞船。* *您需要一个巨大的发动机和大量的燃料，如果您有大型的发动机和少量的燃料，甚至无法起飞。* *要制造火箭，您需要一个巨大的发动机和大量的燃料。* ”(来源： [Wired][] )

In the last we would like to thank our readers, We hope you guys enjoyed this post. Any suggestions or queries are always welcomed from readers end. If there exist any doubt or ambiguity regarding Deep Learning vs Machine Learning, please do let us know in the comment section below. Also let us know what other topics do you want us to write about, we would be more than happy.

最后，我们要感谢我们的读者，希望大家喜欢这篇文章。 任何建议或疑问总是受到读者的欢迎。 如果对深度学习与机器学习存在任何疑问或含糊之处，请在下面的评论部分中告知我们。 也让我们知道您还想写些其他话题，我们会非常高兴。

> 翻译自: [https://www.thecrazyprogrammer.com/2018/03/difference-deep-learning-machine-learning.html][https_www.thecrazyprogrammer.com_2018_03_difference-deep-learning-machine-learning.html]

[Deep Learning]: https://www.thecrazyprogrammer.com/2017/12/introduction-to-deep-learning.html
[Machine Learning]: https://www.thecrazyprogrammer.com/2017/11/introduction-machine-learning.html
[Master in Data Science]: https://intellipaat.com/data-science-architect-masters-program-training/
[Machine learning Course]: https://intellipaat.com/machine-learning-certification-training-course/
[Artificial Intelligence]: https://www.thecrazyprogrammer.com/2018/01/introduction-artificial-intelligence.html
[Neural Networks]: https://www.thecrazyprogrammer.com/2017/12/introduction-neural-networks.html
[Data Strategy]: https://www.bernardmarr.com/default.asp?contentID=620trategy
[Difference between Deep Learning and Machine Learning]: /images/20221123/c27d52c96a474872abf5e7a27fd8dc08.png
[Image Source]: https://www.xenonstack.com/blog/data-science/log-analytics-with-deep-learning-and-machine-learning
[Wired]: https://www.wired.com/brandlab/2015/05/andrew-ng-deep-learning-mandate-humans-not-just-machines/
[https_www.thecrazyprogrammer.com_2018_03_difference-deep-learning-machine-learning.html]: https://www.thecrazyprogrammer.com/2018/03/difference-deep-learning-machine-learning.html