Pratique du Shadowing: 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!)】 - Apprendre l'anglais à l'oral avec la vidéo
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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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Vocabulaire et conseils d’expression pour cette leçon
Cette leçon d’expression orale de niveau C1 s’appuie sur la vidéo « P1 机器学习的应用【2024公认最好的 ». Les mots qui reviennent le plus souvent : machine, learning, algorithms, applications, learn. Cette vidéo contient 43 phrases et 698 mots à répéter en shadowing. La partie parlée dure 4:26. Le locuteur parle à un rythme naturel d’environ 158 mots par minute, proche d’une conversation courante. 85 % des mots font partie des 3 000 mots les plus courants en anglais ; le reste mérite d’être vu avant de commencer.
Vocabulaire clé de cette vidéo
Les 14 mots les plus avancés de la vidéo, avec leur prononciation et leur sens :
| Mot | Prononciation | Sens |
|---|---|---|
| algorithm nom | /ˈælɡəɹɪðm̩/ | algorithme |
| excite verbe | /ɪkˈsaɪt/ | exciter |
| augment verbe | /ɔɡˈmɛnt/ | augmenter |
| dabble verbe | /ˈdæb.əl/ | barboter |
| diagnose verbe | /ˈdaɪ.əɡˌnoʊs/ | diagnostiquer |
| untapped adjectif | inexploité | |
| vastly adverbe | /ˈvæstli/ | beaucoup |
| terminology nom | /ˌtɝ.məˈnɑ.lə.d͡ʒi/ | terminologie |
| worthwhile adjectif | /ˌwɜː(ɹ)θˈwaɪl/ | de valeur, qui en vaut la peine |
| researcher nom | /ˈɹiˌsɝ.t͡ʃɚ/ | chercheur, chercheuse |
| automotive adjectif | /ˌɔtəˈmoʊtɪv/ | automobile, automoteur |
| explicit adjectif | /ɪkˈsplɪs.ɪt/ | explicite |
| someday adverbe | un jour | |
| tremendous adjectif | /tɹɪˈmɛndəs/ | formidable |
Les verbes à particule que vous entendrez
| Mot | Sens |
|---|---|
| grow up verbe | grandir |
| pick up verbe | soulever |
| turn out verbe | se finir, se terminer |
La grammaire de cette vidéo
Les structures que le locuteur utilise le plus, avec les mots exacts de la vidéo :
| Structure | Dans la vidéo |
|---|---|
| Voix passive be + participe passé — l’accent est mis sur ce qui arrive, pas sur qui le fait | is sometimes called · has been overhyped · is estimated |
| Present perfect have/has + participe passé — une action passée qui compte encore maintenant | you've learned · has been overhyped |
Prononciation à surveiller
Le locuteur utilise 7 contractions et formes réduites, comme we'll, don't, I've. Prononcez-les sous leur forme courte, telles que vous les entendez.
- Les sons « th »: algorithm /ˈælɡəɹɪðm̩/, worthwhile /ˌwɜː(ɹ)θˈwaɪl/
- Mots longs — placez bien l’accent: terminology /ˌtɝ.məˈnɑ.lə.d͡ʒi/, automotive /ˌɔtəˈmoʊtɪv/
Les sons difficiles pour les francophones :
- /tʃ/ et /dʒ/ — à ne pas adoucir en « ch » et « j »: terminology /ˌtɝ.məˈnɑ.lə.d͡ʒi/, researcher /ˈɹiˌsɝ.t͡ʃɚ/
- /r/ anglais — langue recourbée, sans frotter la gorge: algorithm /ˈælɡəɹɪðm̩/, worthwhile /ˌwɜː(ɹ)θˈwaɪl/, researcher /ˈɹiˌsɝ.t͡ʃɚ/, tremendous /tɹɪˈmɛndəs/
Comment s’entraîner avec cette vidéo
- Écoutez la vidéo en entier une fois sans parler et notez les mots que vous ne connaissez pas.
- Commencez à la vitesse 0,75×, répétez phrase par phrase, puis revenez à la vitesse normale quand cela devient facile.
- Enregistrez-vous et comparez avec l’original, en faisant attention à des mots comme algorithm, excite, augment.
Qu'est-ce que la technique du Shadowing ?
Le Shadowing est une technique d'apprentissage des langues fondée sur la science, développée à l'origine pour la formation des interprètes professionnels. Le principe est simple mais puissant : vous écoutez de l'anglais natif et le répétez immédiatement à voix haute — comme une ombre suivant le locuteur avec un décalage de 1 à 2 secondes. Les recherches montrent une amélioration significative de la précision de la prononciation, de l'intonation, du rythme, des liaisons, de la compréhension orale et de la fluidité.