跟读练习: 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!)】 - 通过视频学习英语口语
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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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本课的词汇与口语要点
这节 C1 级别的口语课以视频“P1 机器学习的应用【2024公认最好的”为素材。 视频中反复出现的词有：machine, learning, algorithms, applications, learn。 这段视频共有 43 个句子、698 个单词可供跟读。 讲话部分时长为 4:26。 说话人语速自然，每分钟约 158 个词，接近日常对话。 85% 的单词属于英语最常用的 3,000 词，其余的词建议在练习前先学一下。
视频中的重点词汇
视频中最难的 11 个单词，附发音和释义：
| 单词 | 发音 | 释义 |
|---|---|---|
| algorithm 名词 | /ˈælɡəɹɪðm̩/ | 算法, 演算法 |
| excite 动词 | /ɪkˈsaɪt/ | 激動 /激动 |
| dabble 动词 | /ˈdæb.əl/ | 玩水, 濺濕 /溅湿 |
| diagnose 动词 | /ˈdaɪ.əɡˌnoʊs/ | 診斷 /诊断 |
| terminology 名词 | /ˌtɝ.məˈnɑ.lə.d͡ʒi/ | 術語學 /术语学 |
| worthwhile 形容词 | /ˌwɜː(ɹ)θˈwaɪl/ | 值得, 划算 |
| researcher 名词 | /ˈɹiˌsɝ.t͡ʃɚ/ | 研究員 /研究员, 研究者 |
| automotive 形容词 | /ˌɔtəˈmoʊtɪv/ | 汽車 /汽车, 機動車 /机动车 |
| explicit 形容词 | /ɪkˈsplɪs.ɪt/ | 精确 |
| someday 副词 | 日後 /日后, 有一天 | |
| tremendous 形容词 | /tɹɪˈmɛndəs/ | 令人敬畏/驚歎/驚異的 /令人敬畏/惊叹/惊异的 |
视频中出现的短语动词
| 单词 | 释义 |
|---|---|
| grow up 动词 | 長大 /长大 |
| pick up 动词 | 拾取, 撿 /捡 |
| turn out 动词 | 原來 /原来 |
视频中的语法
说话人最常用的结构，并附上视频中的原话：
| 结构 | 视频中的用法 |
|---|---|
| 被动语态 be + 过去分词 — 强调发生了什么，而不是谁做的 | is sometimes called · has been overhyped · is estimated |
| 现在完成时 have/has + 过去分词 — 过去发生但与现在仍有关联的事 | you've learned · has been overhyped |
需要注意的发音
说话人用了 7 次缩略和弱读形式，例如 we'll, don't, I've。请按听到的简短形式来说。
- “th” 音: algorithm /ˈælɡəɹɪðm̩/, worthwhile /ˌwɜː(ɹ)θˈwaɪl/
- 长单词——注意重音位置: terminology /ˌtɝ.məˈnɑ.lə.d͡ʒi/, automotive /ˌɔtəˈmoʊtɪv/
中文母语者容易读错的音：
- /v/ — 不要读成 /w/，上齿轻触下唇: vastly /ˈvæstli/, automotive /ˌɔtəˈmoʊtɪv/
- 词尾辅音 — 要读清楚，后面不要加元音: excite /ɪkˈsaɪt/, augment /ɔɡˈmɛnt/, diagnose /ˈdaɪ.əɡˌnoʊs/, automotive /ˌɔtəˈmoʊtɪv/, explicit /ɪkˈsplɪs.ɪt/
- /r/ 和 /l/ 的区别: algorithm /ˈælɡəɹɪðm̩/, worthwhile /ˌwɜː(ɹ)θˈwaɪl/
如何用这段视频练习
- 先完整听一遍视频，不要开口，记下不认识的单词。
- 先用 0.75 倍速逐句跟读，熟练之后再回到正常速度。
- 录下自己的声音并与原声对比，特别注意 algorithm, excite, dabble 这类单词。
什么是跟读法？
跟读法 (Shadowing) 是一种有科学依据的语言学习技巧，最初开发用于专业口译员的培训，并由多语言者Alexander Arguelles博士普及。这个方法简单而强大：您在听英语母语原声的同时立即大声重复——就像是一个延迟1-2秒紧跟说话者的影子。与被动听力或语法练习不同，跟读法强迫您的大脑和口腔肌肉同时处理并模仿真实的讲话模式。研究表明它能显着提高发音准确性，语调，节奏，连读，听力理解和口语流利度——使其成为雅思口语备考和真实英语交流最有效的方法之一。