ฝึกพูดภาษาอังกฤษด้วยเทคนิค Shadowing จากวิดีโอ: P2 什么是机器学习【2024公认最好的 | 吴恩达机器学习 | 教程 | Machine Learning Specialization(超爽中英!)】
กำลังสร้างบทเรียน...
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So what is machine learning?
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In this video you learn a definition of what it is
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and also get a sense of when you might want to apply it.
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Let's take a look together.
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Here's the definition of what is machine learning that is attributed to Arthur Samuel.
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He defined machine learning as the field of study that gives computers the ability to learn without being explicitly programmed.
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Salva's claim to fame was that back in the 1950s he wrote a checkers playing program.
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And the amazing thing about this program was that Arthur Samuel himself wasn't a very good checkers player.
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What he did was he had programmed a computer to play maybe tens of thousands of games against himself.
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And by watching what sorts of board positions tended to lead to wins and what positions tended to lead to losses,
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the checkers playing program learned over time what are good or bad board positions.
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By trying to get to good and avoid bad positions, his program learned to get better and better at playing checkers.
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Because the computer had the patience to play tens of thousands of games against itself,
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it was able to get so much checkers playing experience that eventually it became a better checkers player than author Samuel himself.
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Now, throughout these videos, besides me trying to talk about stuff,
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I'll occasionally ask you a question to help make sure you understand the content here's one about what happens
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if the computer had played far fewer games please take a look
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and pick whichever you think is a better answer thanks for looking at the quiz and so
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if you had selected this answer would have made it worse
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then you got it right in general the more opportunities you give a learning algorithm to learn, the better it will perform.
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If you didn't select the correct answer the first time, that's totally okay too.
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The point of these quiz questions isn't to see if you can get them all correct on the first try.
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These questions are here just to help you practice the concepts you're learning.
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Author Samuel's definition was a rather informal one, but in the next two videos, we'll dive deeper together into what are the major types types of machine learning algorithms.
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In this class, you learn about many different learning algorithms.
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The two main types of machine learning are supervised learning and unsupervised learning.
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We'll define what these terms mean more in the next couple videos.
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Of these two, supervised learning is the type of machine learning that is used most in many real -world applications,
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and that has seen the most rapid advancement and innovation.
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In this specialization, which has three causes in total, the first and second causes will focus on supervised learning,
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and the third will focus on unsupervised learning.
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You might have also heard of reinforcement learning.
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This is another type of machine learning algorithm that I'll talk about briefly,
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but by far the two most used types of learning algorithms today are supervised learning and unsupervised learning.
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That's why we'll spend most of this class talking about them.
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The other thing we're going to spend a lot of time on in this specialization is practical advice for applying learning algorithms.
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This is something I feel pretty strongly about.
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Teaching about learning algorithms is like giving someone a set of tools
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and equally important
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or even more importance than making sure you have great tools is making sure you know how to apply them
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because you know what good is it
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if someone were to give you a state -of -the -art hammer
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or a state -of -the -art hand drill
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and say good luck now you have all the tools you
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need to build a three -story house It doesn't really work like that.
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And so too in machine learning, making sure you have the tools is really important.
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And so it's making sure that you know how to apply the tools of machine learning effectively.
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So that's what you get in this class, the tools as well as the skills in applying them effectively.
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I regularly visit with friends and teams in some of the top tech companies.
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And even today, I see experienced machine learning teams apply machine learning algorithms to some problems.
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And sometimes they've been going at it for six months without much success.
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And when I look at what they're doing, I sometimes feel like I could have told them six months ago that the current approach won't work.
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And there's a different way of using these tools that will give them a much better chance of success.
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So in this class, one of the relatively unique things you learn is you learn
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a lot about the best practices for how to actually develop a practical, valuable machine learning system.
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This way, you're less likely to end up in one of those teams
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that end up losing six months going in the wrong direction.
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In this class, you gain a sense of how the most skilled machine learning engineers build systems,
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and I hope you finish this class as one of those very rare people in today's world
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that know how to design and build serious machine learning systems.
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So that's machine learning.
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In the next video, let's look more deeply at what is supervised learning and also what is unsupervised learning.
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In addition, you learn when you might want to use each of them, supervised and unsupervised learning.
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I'll see you in the next video.
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คำศัพท์และข้อสังเกตด้านการพูดสำหรับบทเรียนนี้
วิดีโอนี้มี 64 ประโยค และ 927 คำ สำหรับฝึกพูดตาม ช่วงที่มีเสียงพูดยาว 5:36 ผู้พูดพูดด้วยความเร็วเป็นธรรมชาติ ประมาณ 166 คำต่อนาที ใกล้เคียงกับบทสนทนาในชีวิตประจำวัน 88% ของคำอยู่ใน 3,000 คำที่ใช้บ่อยที่สุดในภาษาอังกฤษ ส่วนที่เหลือควรดูไว้ก่อนเริ่มฝึก
คำศัพท์สำคัญในวิดีโอนี้
คำที่ควรเรียนรู้ 10 คำจากวิดีโอ พร้อมคำอ่านและความหมาย:
| คำศัพท์ | คำอ่าน | ความหมาย |
|---|---|---|
| algorithm คำนาม | /ˈælɡəɹɪðm̩/ | ขั้นตอนวิธี, อัลกอริทึม |
| select คำกริยา | /sɪˈlɛkt/ | คัดเลือก |
| fame คำนาม | /feɪm/ | ชื่อเสียง, ศักดิ์ศรี |
| innovation คำนาม | /ˌɪn.əˈveɪ.ʃən/ | นวัตกรรม |
| patience คำนาม | /ˈpeɪ̯.ʃəns/ | ความอดทน |
| hammer คำนาม | /ˈhæmɚ/ | ค้อน, พะเนิน |
| dive คำกริยา | /daɪv/ | ดำ |
| drill คำนาม | /dɹɪl/ | สว่าน |
| informal คำคุณศัพท์ | /ɪnˈfɔɹ.m(ə)l/ | ไม่เป็นทางการ |
| attribute คำนาม | /ˈæt.ɹɪˌbjut/ | คุณลักษณะ, คุณสมบัติ |
การออกเสียงที่ควรระวัง
ผู้พูดใช้รูปย่อและรูปลดเสียง 16 ครั้ง เช่น I'll, we'll, you're ให้พูดแบบสั้นตามที่ได้ยิน
- เสียง “sh” และ “zh”: occasionally /əˈkeɪʒənəli/, innovation /ˌɪn.əˈveɪ.ʃən/, patience /ˈpeɪ̯.ʃəns/, specialization [ˌspɛʃəlaɪ̯ˈzeɪ̯ʃn̩]
- คำยาว — ลงเสียงหนักให้ถูกพยางค์: occasionally /əˈkeɪʒənəli/, innovation /ˌɪn.əˈveɪ.ʃən/, specialization [ˌspɛʃəlaɪ̯ˈzeɪ̯ʃn̩], reinforcement /ˌɹiːɪnˈfɔːsmənt/
วิธีฝึกกับวิดีโอนี้
- ฟังวิดีโอให้จบหนึ่งรอบโดยยังไม่ต้องพูด แล้วจดคำที่ยังไม่รู้จัก
- เริ่มที่ความเร็ว 0.75× พูดตามทีละประโยค แล้วกลับไปใช้ความเร็วปกติเมื่อเริ่มคล่อง
- อัดเสียงตัวเองแล้วเทียบกับต้นฉบับ โดยสังเกตคำอย่าง algorithm, select, fame เป็นพิเศษ
เทคนิค Shadowing คืออะไร?
Shadowing เป็นเทคนิคการเรียนรู้ภาษาที่ได้รับการรับรองทางวิทยาศาสตร์ พัฒนาขึ้นสำหรับการฝึกนักแปลมืออาชีพ วิธีการนี้เรียบง่ายแต่ทรงพลัง: คุณฟังเสียงภาษาอังกฤษจากเจ้าของภาษาและพูดตามทันที — เหมือนเงาที่ตามผู้พูดด้วยช่วงเวลาห่าง 1-2 วินาที การวิจัยแสดงว่าเทคนิคนี้ปรับปรุงความแม่นยำในการออกเสียง ทำนองเสียง จังหวะ การเชื่อมเสียง การฟังเข้าใจ และความคล่องแคล่วในการพูดได้อย่างมีนัยสำคัญ