Tensorflow 简明教程
Machine Learning and Deep Learning
人工智能是近年来最热门的趋势之一。机器学习和深度学习构成人工智能。下面显示的维恩图解释了机器学习和深度学习之间的关系 −
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 −
Machine Learning
机器学习是让计算机按照设计和编程的算法执行操作的科学艺术。许多研究人员认为，机器学习是实现达到人类水平的 AI 的最佳途径。机器学习包括以下类型的模式
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
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Supervised learning pattern
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Unsupervised learning pattern
Deep Learning
深度学习是机器学习的一个子领域，其中涉及的算法受到称为人工神经网络的大脑结构和功能的启发。
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.
当今深度学习的所有价值都是通过监督式学习或从标记数据和算法中学习而获得的。
All the value today of deep learning is through supervised learning or learning from labelled data and algorithms.
深度学习中的每个算法都会经历相同的过程。它包括输入的非线性变换层次结构，可用于生成统计模型作为输出。
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.
考虑定义机器学习过程的以下步骤
Consider the following steps that define the Machine Learning process
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Identifies relevant data sets and prepares them for analysis.
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Chooses the type of algorithm to use
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Builds an analytical model based on the algorithm used.
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Trains the model on test data sets, revising it as needed.
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Runs the model to generate test scores.
Difference between Machine Learning and Deep learning
在本节中，我们将了解机器学习和深度学习之间的差异。
In this section, we will learn about the difference between Machine Learning and Deep Learning.
Amount of data
机器学习处理大量数据。它对于处理少量数据也有用。另一方面，如果数据量快速增长，深度学习将有效。下图显示了机器学习和深度学习与数据量之间如何运作 −
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 −
Hardware Dependencies
与传统机器学习算法不同，深度学习算法被设计为高度依赖于高端机器。深度学习算法执行许多矩阵乘法运算，需要大量的硬件支持。
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.
Feature Engineering
特征工程是将领域知识放入指定特征中以降低数据复杂性并使模式对学习算法可见的过程。
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.
示例 − 传统机器学习模式专注于像素和其他用于特征工程过程的属性。深度学习算法专注于数据中的高级特征。它减少了为每个新问题开发新特征提取器的任务。
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.
Problem Solving Approach
传统机器学习算法遵循标准程序来解决问题。它将问题分解为部分，解决其中每个部分并将它们组合起来以获得所需的结果。深度学习专注于端到端解决问题，而不是将问题分解为各个部分。
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.
Execution Time
执行时间是训练算法所需的时间量。深度学习需要很多时间来训练，因为它包含许多参数，耗时比平时长。相比之下，机器学习算法所需的执行时间较少。
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.
Applications of Machine Learning and Deep Learning
在本节中，我们将了解机器学习和深度学习的不同应用。
In this section, we will learn about the different applications of Machine Learning and Deep Learning.
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Computer vision which is used for facial recognition and attendance mark through fingerprints or vehicle identification through number plate.
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Information Retrieval from search engines like text search for image search.
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Automated email marketing with specified target identification.
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Medical diagnosis of cancer tumors or anomaly identification of any chronic disease.
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Natural language processing for applications like photo tagging. The best example to explain this scenario is used in Facebook.
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Online Advertising.
Future Trends
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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.
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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.
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Machine learning and deep learning will prove beneficial in research and academics field.
Conclusion
本文概述了机器学习和深度学习，并提供了图示，展示了差异，同时重点介绍了未来趋势。许多 AI 应用程序主要利用机器学习算法来实现自助服务、提高代理生产力，并提高工作流的可靠性。机器学习和深度学习算法对许多企业和行业领导者来说是一个激动人心的前景。
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.