📜  机器学习和深度学习之间的区别

📅  最后修改于: 2020-09-29 03:28:28             🧑  作者: Mango

机器学习和深度学习之间的区别

机器学习和深度学习是数据科学的两个主要概念,也是人工智能的子集。大多数人认为机器学习,深度学习以及人工智能是相同的流行语。但实际上,所有这些术语都是不同的,但彼此相关。

在本主题中,我们将学习机器学习与深度学习有何不同。但是在学习差异之前,首先让我们简要介绍一下机器学习和深度学习。

什么是机器学习?

机器学习是人工智能和不断发展的技术的一部分,它使机器能够从过去的数据中学习并自动执行给定的任务。

机器学习使计算机可以自己学习经验,使用统计方法来改进性能并预测输出,而无需进行显式编程。

ML的流行应用是电子邮件垃圾邮件过滤,产品推荐,在线欺诈检测等。

一些有用的ML算法是:

  • 决策树算法
  • 朴素贝叶斯
  • 随机森林
  • K均值聚类
  • KNN算法
  • Apriori算法等

机器学习如何工作?

机器学习模型的工作可以通过识别猫或狗的图像的示例来理解。为了识别这一点,ML模型将猫和狗的图像作为输入,提取图像的不同特征(例如形状,高度,鼻子,眼睛等),应用分类算法,并预测输出。考虑下图:

什么是深度学习?

深度学习是机器学习的子集,或者可以说是一种特殊的机器学习。它在技术上与机器学习的工作方式相同,但是功能和方法不同。它受到称为“神经元”的人脑细胞功能的启发,并导致了人工神经网络的概念。它也称为深度神经网络或深度神经学习。

在深度学习中,模型使用不同的层来学习和发现来自数据的见解。

深度学习的一些流行应用是自动驾驶汽车,语言翻译,自然语言处理等。

一些流行的深度学习模型是:

  • 卷积神经网络
  • 递归神经网络
  • 自动编码器
  • 经典神经网络等

深度学习如何工作?

我们可以通过识别猫还是狗的相同示例来理解深度学习的工作。深度学习模型将图像作为输入,并将其直接馈送到算法,而无需任何手动特征提取步骤。图像传递到人工神经网络的不同层,并预测最终输出。

考虑下图:

机器学习和深度学习之间的主要比较

让我们根据不同的参数了解这两个术语之间的主要区别:

Parameter Machine Learning Deep Learning
Data Dependency Although machine learning depends on the huge amount of data, it can work with a smaller amount of data. Deep Learning algorithms highly depend on a large amount of data, so we need to feed a large amount of data for good performance.
Execution time Machine learning algorithm takes less time to train the model than deep learning, but it takes a long-time duration to test the model. Deep Learning takes a long execution time to train the model, but less time to test the model.
Hardware Dependencies Since machine learning models do not need much amount of data, so they can work on low-end machines. The deep learning model needs a huge amount of data to work efficiently, so they need GPU’s and hence the high-end machine.
Feature Engineering Machine learning models need a step of feature extraction by the expert, and then it proceeds further. Deep learning is the enhanced version of machine learning, so it does not need to develop the feature extractor for each problem; instead, it tries to learn high-level features from the data on its own.
Problem-solving approach To solve a given problem, the traditional ML model breaks the problem in sub-parts, and after solving each part, produces the final result. The problem-solving approach of a deep learning model is different from the traditional ML model, as it takes input for a given problem, and produce the end result. Hence it follows the end-to-end approach.
Interpretation of result The interpretation of the result for a given problem is easy. As when we work with machine learning, we can interpret the result easily, it means why this result occur, what was the process. The interpretation of the result for a given problem is very difficult. As when we work with the deep learning model, we may get a better result for a given problem than the machine learning model, but we cannot find why this particular outcome occurred, and the reasoning.
Type of data Machine learning models mostly require data in a structured form. Deep Learning models can work with structured and unstructured data both as they rely on the layers of the Artificial neural network.
Suitable for Machine learning models are suitable for solving simple or bit-complex problems. Deep learning models are suitable for solving complex problems.

机器学习和深度学习中选择哪一个?

正如我们所看到的,对ML和DL进行了简要介绍并进行了一些比较之后,现在说明为什么以及需要选择哪一个来解决特定问题。因此,可以通过给定的流程图来理解:

因此,如果您有大量数据和高硬件功能,请进行深度学习。但是,如果您没有它们,请选择ML模型来解决您的问题。

结论:总而言之,我们可以说深度学习是具有更多功能和不同工作方式的机器学习。选择其中任何一个以解决特定问题取决于数据量和问题的复杂性。