机器学习深度学习加强学习_机器学习和深度学习

机器学习深度学习加强学习

机器学习和深度学习 (Machine Learning and Deep Learning)

Artificial Intelligence is one of the most popular trends of recent times. Machine learning and deep learning constitute artificial intelligence. The Venn diagram shown below explains the relationship of machine learning and deep learning −

人工智能是近来最受欢迎的趋势之一。 机器学习和深度学习构成了人工智能。 下面显示的维恩图说明了机器学习和深度学习的关系-

机器学习 (Machine Learning)

Machine learning is the art of science of getting computers to act as per the algorithms designed and programmed. Many researchers think machine learning is the best way to make progress towards human-level AI. Machine learning includes the following types of patterns

机器学习是使计算机按照设计和编程的算法运行的科学技术。 许多研究人员认为，机器学习是在人类级AI上取得进步的最好方法。 机器学习包括以下类型的模式

• Supervised learning pattern
  监督学习模式
• Unsupervised learning pattern
  无监督学习模式

深度学习 (Deep Learning)

Deep learning is a subfield of machine learning where concerned algorithms are inspired by the structure and function of the brain called artificial neural networks.

深度学习是机器学习的一个子领域，相关算法受称为人工神经网络的大脑结构和功能的启发。

All the value today of deep learning is through supervised learning or learning from labelled data and algorithms.

如今，深度学习的所有价值在于通过监督学习或从标记的数据和算法中学习。

Each algorithm in deep learning goes through the same process. It includes a hierarchy of nonlinear transformation of input that can be used to generate a statistical model as output.

深度学习中的每种算法都经过相同的过程。 它包括输入的非线性转换层次结构，可用于生成统计模型作为输出。

Consider the following steps that define the Machine Learning process

考虑定义机器学习过程的以下步骤

• Identifies relevant data sets and prepares them for analysis.
  识别相关数据集并准备进行分析。
• Chooses the type of algorithm to use
  选择要使用的算法类型
• Builds an analytical model based on the algorithm used.
  基于所使用的算法构建分析模型。
• Trains the model on test data sets, revising it as needed.
  在测试数据集上训练模型，并根据需要对其进行修改。
• Runs the model to generate test scores.
  运行模型以生成测试分数。

机器学习和深度学习之间的区别 (Difference between Machine Learning and Deep learning)

In this section, we will learn about the difference between Machine Learning and Deep Learning.

在本节中，我们将学习机器学习和深度学习之间的区别。

数据量 (Amount of data)

Machine learning works with large amounts of data. It is useful for small amounts of data too. Deep learning on the other hand works efficiently if the amount of data increases rapidly. The following diagram shows the working of machine learning and deep learning with the amount of data −

机器学习处理大量数据。 它对于少量数据也很有用。 另一方面，如果数据量Swift增加，则深度学习将有效地工作。 下图显示了使用数据量的机器学习和深度学习的工作-

硬件依赖性 (Hardware Dependencies)

Deep learning algorithms are designed to heavily depend on high-end machines unlike the traditional machine learning algorithms. Deep learning algorithms perform a number of matrix multiplication operations, which require a large amount of hardware support.

与传统的机器学习算法不同，深度学习算法被设计为严重依赖高端机器。 深度学习算法执行许多矩阵乘法运算，这需要大量的硬件支持。

特征工程 (Feature Engineering)

Feature engineering is the process of putting domain knowledge into specified features to reduce the complexity of data and make patterns that are visible to learning algorithms it works.

特征工程是将领域知识放入指定特征中的过程，以降低数据的复杂性并创建对其有效的学习算法可见的模式。

Example − Traditional machine learning patterns focus on pixels and other attributes needed for feature engineering process. Deep learning algorithms focus on high-level features from data. It reduces the task of developing new feature extractor of every new problem.

示例-传统的机器学习模式着重于特征工程过程所需的像素和其他属性。 深度学习算法专注于数据的高级功能。 它减少了开发每个新问题的新特征提取器的任务。

解决问题的方法 (Problem Solving Approach)

The traditional machine learning algorithms follow a standard procedure to solve the problem. It breaks the problem into parts, solve each one of them and combine them to get the required result. Deep learning focusses in solving the problem from end to end instead of breaking them into divisions.

传统的机器学习算法遵循标准程序来解决该问题。 它将问题分解成多个部分，解决每个问题，然后将它们组合起来以获得所需的结果。 深度学习的重点是从头到尾解决问题，而不是将其分成多个部分。

执行时间处理时间 (Execution Time)

Execution time is the amount of time required to train an algorithm. Deep learning requires a lot of time to train as it includes a lot of parameters which takes a longer time than usual. Machine learning algorithm comparatively requires less execution time.

执行时间是训练算法所需的时间。 深度学习需要大量的时间进行训练，因为它包含许多参数，比平时需要更长的时间。 机器学习算法所需的执行时间相对较少。

可解释性 (Interpretability)

Interpretability is the major factor for comparison of machine learning and deep learning algorithms. The main reason is that deep learning is still given a second thought before its usage in industry.

可解释性是比较机器学习和深度学习算法的主要因素。 主要原因是，深度学习在应用于工业之前还需要重新考虑。

机器学习和深度学习的应用 (Applications of Machine Learning and Deep Learning)

In this section, we will learn about the different applications of Machine Learning and Deep Learning.

在本部分中，我们将学习机器学习和深度学习的不同应用。

• Computer vision which is used for facial recognition and attendance mark through fingerprints or vehicle identification through number plate.

  计算机视觉用于通过指纹进行面部识别和考勤标记，或通过车牌进行车辆识别。

• Information Retrieval from search engines like text search for image search.

  从搜索引擎(如文本搜索到图像搜索)检索信息。

• Automated email marketing with specified target identification.

  具有指定目标标识的自动电子邮件营销。

• Medical diagnosis of cancer tumors or anomaly identification of any chronic disease.

  癌症肿瘤的医学诊断或任何慢性疾病的异常识别。

• Natural language processing for applications like photo tagging. The best example to explain this scenario is used in Facebook.

  用于照片标记等应用程序的自然语言处理。 在Facebook中使用了解释这种情况的最佳示例。

• Online Advertising.

  在线广告。

未来的趋势 (Future Trends)

• With the increasing trend of using data science and machine learning in the industry, it will become important for each organization to inculcate machine learning in their businesses.

  随着行业中使用数据科学和机器学习的趋势不断增加，对于每个组织而言，在其业务中灌输机器学习将变得很重要。

• Deep learning is gaining more importance than machine learning. Deep learning is proving to be one of the best techniques in state-of-art performance.

  深度学习比机器学习变得越来越重要。 事实证明，深度学习是最新性能的最佳技术之一。

• Machine learning and deep learning will prove beneficial in research and academics field.

  机器学习和深度学习将在研究和学术领域证明是有益的。

结论 (Conclusion)

In this article, we had an overview of machine learning and deep learning with illustrations and differences also focusing on future trends. Many of AI applications utilize machine learning algorithms primarily to drive self-service, increase agent productivity and workflows more reliable. Machine learning and deep learning algorithms include an exciting prospect for many businesses and industry leaders.

在本文中，我们对机器学习和深度学习进行了概述，并提供了插图和差异，并着眼于未来的趋势。 许多AI应用程序主要利用机器学习算法来驱动自助服务，提高代理生产力和工作流程更可靠。 机器学习和深度学习算法为许多企业和行业领导者带来了令人兴奋的前景。

翻译自: https://www.tutorialspoint.com/tensorflow/tensorflow_machine_learning_deep_learning.htm

机器学习深度学习加强学习