跟讀練習: P1 机器学习的应用【2024公认最好的 | 吴恩达机器学习 | 教程 | Machine Learning Specialization(超爽中英!)】 - 通過影片學習英語口語
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43 句
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In this class, you learn about the state of the art and also practice implementing machine learning algorithms yourself.
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You learn about the most important machine learning algorithms, some of which are exactly what's being used in large AI or large tech companies today,
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and you get a sense of what is the state of the art in AI.
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Beyond learning the algorithms, though, in this class, you also learn all the important practical tips and tricks for making them perform well,
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and you get to implement them and see how they work for yourself.
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So why is machine learning so widely used today?
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Machine learning had grown up as a subfield of AI or artificial intelligence.
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We wanted to build intelligent machines, and it turns out that there are a few basic things that we could program a machine to do, such as how to find the shortest path from A to B,
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like in your GPS.
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But for the most part, we just did not know how to write an explicit program to do many of the more interesting things, such as perform web search,
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recognize human speech, diagnose diseases from x-rays, or build a self-driving car.
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The only way we knew how to do these things was to have a machine learn to do it by itself.
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For me, when I founded and was leading the Google Brain team, I worked on problems like speech recognition, computer vision for Google Maps,
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street view images, and advertising.
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Or leading AI at Baidu, I worked on everything from AI for augmented reality to combating payment fraud to leading a self-driving car team.
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Most recently, Atlantic AI, AI funded at Stanford University, I've been getting to work on AI applications in manufacturing,
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large-scale agriculture, healthcare, e-commerce, and other problems.
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Today, there are hundreds of thousands, perhaps millions of people, working on machine learning applications who could tell you similar stories about their work with machine learning.
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When you've learned these skills, I hope that you too will find it great fun to dabble in exciting different applications and maybe even different industries.
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In fact, I find it hard to think of any industry
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that machine learning is unlikely to touch in a significant way now or in the near future.
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Looking even further into the future, many people, including me, are excited about the AI dream of someday building machines as intelligent as you or me.
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This is sometimes called Artificial General Intelligence, or AGI.
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I think AGI has been overhyped and we're still a long way away from that goal.
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I don't know if it'll take 50 years or 500 years or longer to get there,
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but most AI researchers believe that the best way to get closer to that goal is by using learning algorithms,
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maybe ones that take some inspiration from how the human brain works.
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You also hear a little more about this quest for AGI later in this course.
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According to a study by McKinsey,
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AI and machine learning is estimated to create an additional $13 trillion of value annually by the year 2030.
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Even though machine learning is already creating tremendous amounts of value in the software industry, I think there could be even vastly greater value
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that is yet to be created outside the software industry in sectors such as retail,
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travel, transportation, automotive, materials, manufacturing, and so on.
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Because of the massive untapped opportunities across so many different sectors,
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today there is a vast unfulfilled demand for this skill set.
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That's why this is such a great time to be learning about machine learning.
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If you find machine learning applications exciting, I hope you stick with me through this class.
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I can almost guarantee that you find mastering these skills worthwhile.
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In the next video, we'll look at a more formal definition of what is machine learning,
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and we'll begin to talk about the main types of machine learning problems and algorithms.
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You pick up some of the main machine learning terminology
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and start to get a sense of what are the different algorithms and when each one might be appropriate.
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So let's go on to the next video.
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本課的詞彙與口語要點
這節 C1 級別的口語課以影片“P1 機器學習的應用【2024公認最好的”為素材。 影片中反覆出現的詞有：machine, learning, algorithms, applications, learn。 這段影片共有 43 個句子、698 個單詞可供跟讀。 講話部分時長為 4:26。 說話人語速自然，每分鐘約 158 個詞，接近日常對話。 85% 的單詞屬於英語最常用的 3,000 詞，其餘的詞建議在練習前先學一下。
影片中的重點詞彙
影片中最難的 11 個單詞，附發音和釋義：
| 單詞 | 發音 | 釋義 |
|---|---|---|
| algorithm 名詞 | /ˈælɡəɹɪðm̩/ | 演算法, 演算法 |
| excite 動詞 | /ɪkˈsaɪt/ | 激動 /激動 |
| dabble 動詞 | /ˈdæb.əl/ | 玩水, 濺濕 /濺溼 |
| diagnose 動詞 | /ˈdaɪ.əɡˌnoʊs/ | 診斷 /診斷 |
| terminology 名詞 | /ˌtɝ.məˈnɑ.lə.d͡ʒi/ | 術語學 /術語學 |
| worthwhile 形容詞 | /ˌwɜː(ɹ)θˈwaɪl/ | 值得, 划算 |
| researcher 名詞 | /ˈɹiˌsɝ.t͡ʃɚ/ | 研究員 /研究員, 研究者 |
| automotive 形容詞 | /ˌɔtəˈmoʊtɪv/ | 汽車 /汽車, 機動車 /機動車 |
| explicit 形容詞 | /ɪkˈsplɪs.ɪt/ | 精確 |
| someday 副詞 | 日後 /日後, 有一天 | |
| tremendous 形容詞 | /tɹɪˈmɛndəs/ | 令人敬畏/驚歎/驚異的 /令人敬畏/驚歎/驚異的 |
影片中出現的短語動詞
| 單詞 | 釋義 |
|---|---|
| grow up 動詞 | 長大 /長大 |
| pick up 動詞 | 拾取, 撿 /撿 |
| turn out 動詞 | 原來 /原來 |
影片中的語法
說話人最常用的結構，並附上影片中的原話：
| 結構 | 影片中的用法 |
|---|---|
| 被動語態 be + 過去分詞 — 強調發生了什麼，而不是誰做的 | is sometimes called · has been overhyped · is estimated |
| 現在完成時 have/has + 過去分詞 — 過去發生但與現在仍有關聯的事 | you've learned · has been overhyped |
需要注意的發音
說話人用了 7 次縮略和弱讀形式，例如 we'll, don't, I've。請按聽到的簡短形式來說。
- “th” 音: algorithm /ˈælɡəɹɪðm̩/, worthwhile /ˌwɜː(ɹ)θˈwaɪl/
- 長單詞——注意重音位置: terminology /ˌtɝ.məˈnɑ.lə.d͡ʒi/, automotive /ˌɔtəˈmoʊtɪv/
中文母語者容易讀錯的音：
- /v/ — 不要讀成 /w/，上齒輕觸下唇: vastly /ˈvæstli/, automotive /ˌɔtəˈmoʊtɪv/
- 詞尾子音 — 要讀清楚，後面不要加母音: excite /ɪkˈsaɪt/, augment /ɔɡˈmɛnt/, diagnose /ˈdaɪ.əɡˌnoʊs/, automotive /ˌɔtəˈmoʊtɪv/, explicit /ɪkˈsplɪs.ɪt/
- /r/ 和 /l/ 的區別: algorithm /ˈælɡəɹɪðm̩/, worthwhile /ˌwɜː(ɹ)θˈwaɪl/
如何用這段影片練習
- 先完整聽一遍影片，不要開口，記下不認識的單詞。
- 先用 0.75 倍速逐句跟讀，熟練之後再回到正常速度。
- 錄下自己的聲音並與原聲對比，特別注意 algorithm, excite, dabble 這類單詞。
什麼是跟讀法？
跟讀法 (Shadowing) 是一種有科學依據的語言學習技巧，最初開發用於專業口譯員的培訓，並由多語言者Alexander Arguelles博士普及。這個方法簡單而強大：您在聽英語母語原聲的同時立即大聲重複——就像是一個延遲1-2秒緊跟說話者的影子。與被動聽力或語法練習不同，跟讀法強迫您的大腦和口腔肌肉同時處理並模模擬實的講話模式。研究表明它能顯著提高發音準確性，語調，節奏，連讀，聽力理解和口語流利度——使其成為雅思口語備考和真實英語交流最有效的方法之一。