機器學習深度學習加強學習_機器學習和深度學習

機器學習深度學習加強學習

機器學習和深度學習 (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 −

人工智能是近來最受歡迎的趨勢之一。 機器學習和深度學習構成了人工智能。 下面顯示的維恩圖說明了機器學習和深度學習的關系-

機器學習 (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

機器學習是使計算機按照設計和程式設計的算法運作的科學技術。 許多研究人員認為，機器學習是在人類級AI上取得進步的最好方法。 機器學習包括以下類型的模式

• Supervised learning pattern

  監督學習模式

• Unsupervised learning pattern

  無監督學習模式

深度學習 (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.

深度學習是機器學習的一個子領域，相關算法受稱為人工神經網絡的大腦結構和功能的啟發。

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

如今，深度學習的所有價值在于通過監督學習或從标記的資料和算法中學習。

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.

深度學習中的每種算法都經過相同的過程。 它包括輸入的非線性轉換層次結構，可用于生成統計模型作為輸出。

Consider the following steps that define the Machine Learning process

考慮定義機器學習過程的以下步驟

• Identifies relevant data sets and prepares them for analysis.

  識别相關資料集并準備進行分析。

• Chooses the type of algorithm to use

  選擇要使用的算法類型

• Builds an analytical model based on the algorithm used.

  基于所使用的算法建構分析模型。

• Trains the model on test data sets, revising it as needed.

  在測試資料集上訓練模型，并根據需要對其進行修改。

• Runs the model to generate test scores.

  運作模型以生成測試分數。

機器學習和深度學習之間的差別 (Difference between Machine Learning and Deep learning)

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

在本節中，我們将學習機器學習和深度學習之間的差別。

資料量 (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 −

機器學習處理大量資料。 它對于少量資料也很有用。 另一方面，如果資料量Swift增加，則深度學習将有效地工作。 下圖顯示了使用資料量的機器學習和深度學習的工作-

硬體依賴性 (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.

與傳統的機器學習算法不同，深度學習算法被設計為嚴重依賴高端機器。 深度學習算法執行許多矩陣乘法運算，這需要大量的硬體支援。

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.

特征工程是将領域知識放入指定特征中的過程，以降低資料的複雜性并建立對其有效的學習算法可見的模式。

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-傳統的機器學習模式着重于特征工程過程所需的像素和其他屬性。 深度學習算法專注于資料的進階功能。 它減少了開發每個新問題的新特征提取器的任務。

解決問題的方法 (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.

傳統的機器學習算法遵循标準程式來解決該問題。 它将問題分解成多個部分，解決每個問題，然後将它們組合起來以獲得所需的結果。 深度學習的重點是從頭到尾解決問題，而不是将其分成多個部分。

執行時間處理時間 (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.

執行時間是訓練算法所需的時間。 深度學習需要大量的時間進行訓練，因為它包含許多參數，比平時需要更長的時間。 機器學習算法所需的執行時間相對較少。

可解釋性 (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.

可解釋性是比較機器學習和深度學習算法的主要因素。 主要原因是，深度學習在應用于工業之前還需要重新考慮。

機器學習和深度學習的應用 (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，我們将學習機器學習和深度學習的不同應用。

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

  計算機視覺用于通過指紋進行面部識别和考勤标記，或通過車牌進行車輛識别。

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

  從搜尋引擎(如文本搜尋到圖像搜尋)檢索資訊。

• Automated email marketing with specified target identification.

  具有指定目标辨別的自動電子郵件營銷。

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

  癌症惡性良性腫瘤的醫學診斷或任何慢性疾病的異常識别。

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

  用于照片标記等應用程式的自然語言處理。 在Facebook中使用了解釋這種情況的最佳示例。

• Online Advertising.

  線上廣告。

未來的趨勢 (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.

  随着行業中使用資料科學和機器學習的趨勢不斷增加，對于每個組織而言，在其業務中灌輸機器學習将變得很重要。

• 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.

  深度學習比機器學習變得越來越重要。 事實證明，深度學習是最新性能的最佳技術之一。

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

  機器學習和深度學習将在研究和學術領域證明是有益的。

結論 (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，我們對機器學習和深度學習進行了概述，并提供了插圖和差異，并着眼于未來的趨勢。 許多AI應用程式主要利用機器學習算法來驅動自助服務，提高代理生産力和工作流程更可靠。 機器學習和深度學習算法為許多企業和行業上司者帶來了令人興奮的前景。

翻譯自: https://www.tutorialspoint.com/tensorflow/tensorflow_machine_learning_deep_learning.htm

機器學習深度學習加強學習