你可能已經(jīng)聽說過深度學(xué)習(xí)并認(rèn)為它是駭人的數(shù)據(jù)科學(xué)里的一個(gè)領(lǐng)域。怎么可能讓機(jī)器像人類一樣學(xué)習(xí)呢?再者，對于某些人而言，更為駭人的是，我們?yōu)槭裁匆�讓機(jī)器展現(xiàn)出類人的行為?這里，請看深度學(xué)習(xí)在實(shí)際應(yīng)用中的十大案例，以便將其潛能視覺化。

深度學(xué)習(xí)是什么?

機(jī)器學(xué)習(xí)和深度學(xué)習(xí)都是人工智能的分支，但深度學(xué)習(xí)是機(jī)器學(xué)習(xí)的進(jìn)一步深化。在機(jī)器學(xué)習(xí)中，由人類程序員設(shè)計(jì)的算法負(fù)責(zé)分析、研究數(shù)據(jù)，然后根據(jù)數(shù)據(jù)分析和研究作出決策。深度學(xué)習(xí)通過一個(gè)人造的神經(jīng)網(wǎng)絡(luò)來學(xué)習(xí)，這一人造神經(jīng)網(wǎng)絡(luò)運(yùn)轉(zhuǎn)起來與人類大腦非常相似，它可以讓機(jī)器在一個(gè)框架內(nèi)像人一樣進(jìn)行分析數(shù)據(jù)。深度學(xué)習(xí)的機(jī)器不需要人類程序員告訴他們要用數(shù)據(jù)做什么，這得賴于我們收集并消耗了大量的數(shù)據(jù)——數(shù)據(jù)是深入學(xué)習(xí)模型的燃料。

深度學(xué)習(xí)的十大應(yīng)用案例

1. Customer experience 用戶體驗(yàn)

機(jī)器學(xué)習(xí)已經(jīng)被很多企業(yè)用來改善用戶體驗(yàn)。部分案例諸如在線自助服務(wù)方案、定制靠譜的工作流程，部分聊天機(jī)器人等都已運(yùn)用到深度學(xué)習(xí)模型。隨著深度學(xué)習(xí)發(fā)展日趨成熟，我們可以預(yù)期，未來這一領(lǐng)域?qū)⒈桓�多企業(yè)用來改善用戶體驗(yàn)。

2、 Translations 翻譯

盡管自動(dòng)機(jī)器翻譯并不新鮮，但深度學(xué)習(xí)正著力于使用神經(jīng)網(wǎng)絡(luò)的堆疊網(wǎng)絡(luò)和圖像翻譯來增強(qiáng)文本的自動(dòng)翻譯。

3、 Adding color to black-and-white images and videos 為黑白圖像、視頻著色

過去，人們手動(dòng)為黑白圖像及視頻著色的過程往往曠日持久，如今，這一工作可以完全由深度學(xué)習(xí)模型自動(dòng)完成。

4、 Language recognition 語言識(shí)別

目前，深度學(xué)習(xí)機(jī)器開始致力于辨別不同的方言。機(jī)器確定某人說的是英語，然后利用AI學(xué)習(xí)辨別方言之間的差異。一旦確定是某種方言，另一個(gè)AI會(huì)繼續(xù)專研這種方言，而這所有的過程均不需要人類參與。

5、 Autonomous vehicles 自動(dòng)駕駛汽車

自動(dòng)駕駛汽車在街上行駛時(shí)，并不只有一個(gè)AI模型在起作用。一些深度學(xué)習(xí)模型專門研究街道標(biāo)識(shí)，而另一些則訓(xùn)練識(shí)別行人。當(dāng)一輛自動(dòng)駕駛的汽車在公路上行駛時(shí)，它將接收到成千上萬條人工智能模型的信息來輔助其行駛。

6、 Computer vision 計(jì)算機(jī)視覺

在圖片分類、目標(biāo)檢測、圖片復(fù)原和分割方面，深度學(xué)習(xí)已經(jīng)展現(xiàn)出超越人類的精確性——他們甚至能識(shí)別手寫的數(shù)字。深度學(xué)習(xí)借助龐大的神經(jīng)網(wǎng)絡(luò)，利用機(jī)器自動(dòng)化人類視覺系統(tǒng)所執(zhí)行的任務(wù)。

7、 Text generation 創(chuàng)作文本

機(jī)器可以學(xué)習(xí)一段文本的標(biāo)點(diǎn)、語法和風(fēng)格，然后利用這個(gè)模式自動(dòng)創(chuàng)作一篇全新的文章，這篇文章的拼寫和語法都是正確的且風(fēng)格與樣本文章一致。從莎士比亞到維基百科，所有的文章都能由此創(chuàng)作。

8、 Image caption generation 生成圖片標(biāo)題

深度學(xué)習(xí)另一個(gè)能力也著實(shí)備受矚目——識(shí)別圖像，并創(chuàng)建一個(gè)符合語句結(jié)構(gòu)的連貫標(biāo)題，宛如人寫的一樣。

9、News aggregator based on sentiment 基于情感的新聞聚合器

如果你想要過濾掉消極新聞，不讓它們進(jìn)入你的世界，先進(jìn)的自然語言處理程序和深度學(xué)習(xí)可以幫助你。使用這種新技術(shù)的新聞聚合器能夠基于用戶情感過濾新聞，因此你可以創(chuàng)建只報(bào)道正面消息的新聞流。

10、 Deep-learning robots 深度學(xué)習(xí)機(jī)器人

機(jī)器人的深度學(xué)習(xí)應(yīng)用程序豐富而強(qiáng)大，它來自一個(gè)令人印象深刻的深度學(xué)習(xí)系統(tǒng)。通過觀察人類完成任務(wù)的行為機(jī)器人就能學(xué)會(huì)家務(wù)，并通過幾個(gè)其他人工智能的輸入來進(jìn)行操作。就像人類大腦如何處理來自過去的經(jīng)驗(yàn)、當(dāng)前的感官以及任何附加數(shù)據(jù)信息一樣，深度學(xué)習(xí)模型將幫助機(jī)器人執(zhí)行基于多個(gè)不同人工智能意見輸入的任務(wù)。

深度學(xué)習(xí)模型的增長被寄予厚望：在未來幾年里將加速發(fā)展，創(chuàng)造更具創(chuàng)新性的應(yīng)用程序。

原文：10 Amazing Examples Of How Deep Learning AI Is Used In Practice?

Bernard Marr

You may have heard about deep learning and felt like it was an area of data science that is incredibly intimidating. How could you possibly get machines to learn like humans? And, an even scarier notion for some, why would we want machines to exhibit human-like behavior? Here, we look at 10 examples of how deep learning is used in practice that will help you visualize the potential.

 

 

What is deep learning?

Both machine and deep learning are subsets of artificial intelligence, but deep learning represents the next evolution of machine learning. In machine learning, algorithms created by human programmers are responsible for parsing and learning from the data. They make decisions based on what they learn from the data. Deep learning learns through an artificial neural network that acts very much like a human brain and allows the machine to analyze data in a structure very much as humans do. Deep learning machines don't require a human programmer to tell them what to do with the data. This is made possible by the extraordinary amount of data we collect and consume—data is the fuel for deep-learning models. For more on what deep learning is please check out my previous post here.

10 ways deep learning is used in practice

Customer experience

Machine learning is already used by many businesses to enhance the customer experience. Just a couple of examples include online self-service solutions and to create reliable workflows. There are already deep-learning models being used for chatbots, and as deep learning continues to mature, we can expect this to be an area deep learning will be used for many businesses.

Translations

Although automatic machine translation isn’t new, deep learning is helping enhance automatic translation of text by using stacked networks of neural networks and allowing translations from images.

Adding color to black-and-white images and videos

What used to be a very time-consuming process where humans had to add color to black-and-white images and videos by hand can now be automatically done with deep-learning models.

Language recognition

Deep learning machines are beginning to differentiate dialects of a language. A machine decides that someone is speaking English and then engages an AI that is learning to tell the differences between dialects. Once the dialect is determined, another AI will step in that specializes in that particular dialect. All of this happens without involvement from a human.

Autonomous vehicles

There's not just one AI model at work as an autonomous vehicle drives down the street. Some deep-learning models specialize in streets signs while others are trained to recognize pedestrians. As a car navigates down the road, it can be informed by up to millions of individual AI models that allow the car to act.

Computer vision

Deep learning has delivered super-human accuracy for image classification, object detection, image restoration and image segmentation—even handwritten digits can be recognized. Deep learning using enormous neural networks is teaching machines to automate the tasks performed by human visual systems.

Text generation

The machines learn the punctuation, grammar and style of a piece of text and can use the model it developed to automatically create entirely new text with the proper spelling, grammar and style of the example text. Everything from Shakespeare to Wikipedia entries have been created.

Image caption generation

Another impressive capability of deep learning is to identify an image and create a coherent caption with proper sentence structure for that image just like a human would write.

News aggregator based on sentiment

When you want to filter out the negative coming to your world, advanced natural language processing and deep learning can help. News aggregators using this new technology can filter news based on sentiment, so you can create news streams that only cover the good news happening.

Deep-learning robots

Deep-learning applications for robots are plentiful and powerful from an impressive deep-learning system that can teach a robot just by observing the actions of a human completing a task to a housekeeping robot that’s provided with input from several other AIs in order to take action. Just like how a human brain processes input from past experiences, current input from senses and any additional data that is provided, deep-learning models will help robots execute tasks based on the input of many different AI opinions.

The growth of deep-learning models is expected to accelerate and create even more innovative applications in the next few years.

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