쉐도잉 연습: C4W1L01 Computer Vision - 영상으로 영어 말하기 배우기
레슨 만드는 중...
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Welcome to this course on convolutional networks.
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Computer vision is one of the areas that's been advancing rapidly thanks to deep learning.
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Deep learning computer vision is now helping self-driving cars figure out where are the other cars
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and the pedestrians around it so it's avoid them.
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It's making face recognition work much better than ever before so
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that perhaps some of you will soon or perhaps already be able to unlock a phone, unlock even a door using just your face.
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And if you look on your cell phone, I bet you have many apps that show you pictures of food
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or pictures of a hotel or just fun pictures of scenery.
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And some of the companies that build those apps are using deep learning to help show you the most attractive, the most beautiful, or the most relevant pictures.
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And I think deep learning is even enabling new types of art to be created.
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So I think the two reasons I'm excited about deep learning for computer vision and why I think you might be too.
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First, rapid advances in computer vision are enabling brand new applications to be built that just weren't possible a few years ago.
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And by learning these tools, perhaps you will be able to invent some of these new products and applications.
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Second, even if you don't end up building computer vision systems per se, I found that because the computer vision research community has been
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so creative and so inventive and coming up with new neural network architectures and algorithms, algorithms is actually inspired a lot of cross fertilization into other areas as well.
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For example, when I was working on speech recognition, I sometimes actually took inspiration from ideas from computer vision and borrowed them into the speech literature.
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So even if you don't end up working on computer vision, I hope
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that you find some of the ideas you learned about in this course helpful for some of your algorithms and your architectures.
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So with that, let's get started.
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Here are some examples of computer vision problems we'll study in this course.
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You've already seen image classification, sometimes also called image recognition, where you might take as input,
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say a 64 by 64 image and try to figure out is that a cat.
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Another example of a computer vision problem is object detection.
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So if you're building a self-driving car, maybe you don't just need to figure out if there are other cars in this image,
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but instead you need to figure out the position of the other cars in this picture
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so that your car can avoid them.
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So in object detection, usually we have to not just figure out that these other objects, say cause in the picture, but also draw boxes around them.
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We'll have some other way of recognizing where in the picture are these objects.
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And notice also in this example that there can be multiple calls in the same picture, or at least every one of them within a certain distance of your call.
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Here's another example, maybe a more fun one is neural style transfer.
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Let's say you have a picture and you want this picture repainted in a different style.
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So, neural style transfer, you have a content image and you have a style image.
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The image on the right is actually a Picasso.
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And you can have a neural network, put them together to repaint the content image that is the image on the left, but in the style of the image on the right,
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and you end up with the image at the bottom.
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So albums like this are enabling new types of artwork to be created.
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And in this course, you learn how to do this yourself as well.
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One of the challenges of computer vision problems is that the inputs can get really big.
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For example, in previous courses, you've worked with 64 by 64 images.
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And so that's 64 by 64 by 3 because there's three color channels.
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And if you multiply that out, that's 12288.
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So X, the input features has dimension 12288.
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And that's not too bad.
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But 64 by 64 is actually a very small image.
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If you work with larger images, maybe this is a thousand pixel by thousand pixel image, and that's actually just one megapixel,
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but the dimension of the input features will be thousand by thousand by three because you have three RGB channels,
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and that's three million.
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And if you're viewing this on a smaller screen, this might not be apparent, but this is actually a low-res 64 by 64 image, and this is a higher-res thousand by thousand image.
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But if you have 3 million input features, then this means that x here will be 3 million dimensional.
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And so if in the first hidden layer, maybe you have just a thousand hidden units,
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then the total number of weights that is the matrix W1,
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if you use a standard fully connected network like we have in causes one or two,
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this matrix will be a 1000 by 3 million dimensional matrix.
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Because X is now R by 3 million, 3M, I'm using it to denote 3 million.
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And this means that this matrix here will have 3 billion parameters, which is just very, very large.
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And with that many parameters, it's difficult to get enough data to prevent the neural network from overfitting,
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and also the computational requirements and memory requirements to train a neural network with three billion parameters is just a bit infeasible.
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But for computer vision applications, you don't want to be stuck using only tiny little images.
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You want to use large images.
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To do that, you need to implement the convolution operation, which is one of the fundamental building blocks of convolutional neural networks.
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Let's see what this means and how you can implement this in the next video.
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I will illustrate convolutions using the example of edge detection.
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이 레슨의 어휘와 말하기 포인트
이 영상에는 섀도잉할 문장 62개와 단어 981개가 있습니다. 말하는 구간의 길이는 5:44입니다. 화자는 분당 약 171단어로, 일상 대화에 가까운 자연스러운 속도로 말합니다. 단어의 86%가 영어에서 가장 많이 쓰이는 3,000단어에 속합니다. 나머지는 연습 전에 미리 확인해 두세요.
이 영상의 핵심 어휘
영상에 나오는 익혀 둘 만한 단어 15개를 발음, 뜻과 함께 정리했습니다.
| 단어 | 발음 | 뜻 |
|---|---|---|
| recognition 명사 | /ˌɹɛkəɡˈnɪʃ(ə)n/ | 인식 |
| detection 명사 | /dɪˈtɛk.ʃən/ | 탐지, 수색 |
| algorithm 명사 | /ˈælɡəɹɪðm̩/ | 알고리즘, 알고리듬 |
| parameter 명사 | /pəˈɹæm.ə.tɚ/ | 파라미터, 매개변수 |
| architecture 명사 | /ˈɑː.kɪˌtɛk.t͡ʃə/ | 건축, 건축학 |
| requirement 명사 | /ɹɪˈkwaɪɹmənt/ | 요구 |
| dimension 명사 | /daɪˈmɛn.ʃən/ | 치수 |
| pixel 명사 | /ˈpɪk.səl/ | 픽셀, 화소 |
| inspiration 명사 | /ˌɪn.spɪˈɹeɪ.ʃən/ | 흡입, 들이쉬기 |
| layer 명사 | /ˈleɪ̯ɚ/ | 층 |
| connect 동사 | /kəˈnɛkt/ | 잇다, 연결하다 |
| rapid 형용사 | /ˈɹæp.ɪd/ | 빠르다 |
| rapidly 부사 | /ˈɹæpɪdli/ | 급속(急速)히 |
| fundamental 형용사 | /ˌfʌn.dəˈmɛn.təl/ | 기본적 |
| classification 명사 | /ˌklæsɪfɪˈkeɪʃən/ | 분류 |
이 영상의 문법
화자가 가장 많이 쓰는 문형을 영상 속 실제 표현과 함께 정리했습니다.
| 문형 | 영상 속 표현 |
|---|---|
| 수동태 be + 과거분사 — 누가 하는지보다 무슨 일이 일어나는지에 초점 | be created · be built · is actually inspired |
| 현재완료 have/has + 과거분사 — 과거의 일이 지금도 관련이 있을 때 | has been · You've already seen · you've worked |
| 관계절 who / which + 절 — 사람이나 사물에 대한 추가 정보 | parameters, which is · operation, which is |
주의할 발음
화자는 don't, I'm, we'll 같은 축약형과 약화된 형태를 13번 사용합니다. 들리는 대로 짧게 발음하세요.
- “sh”와 “zh” 소리: recognition /ˌɹɛkəɡˈnɪʃ(ə)n/, detection /dɪˈtɛk.ʃən/, dimensional /daɪˈmɛn.ʃə.nəl/, dimension /daɪˈmɛn.ʃən/, inspiration /ˌɪn.spɪˈɹeɪ.ʃən/
- 긴 단어 — 강세 위치에 주의: recognition /ˌɹɛkəɡˈnɪʃ(ə)n/, parameter /pəˈɹæm.ə.tɚ/, architecture /ˈɑː.kɪˌtɛk.t͡ʃə/, dimensional /daɪˈmɛn.ʃə.nəl/, inspiration /ˌɪn.spɪˈɹeɪ.ʃən/
이 영상으로 연습하는 방법
- 먼저 말하지 않고 영상을 끝까지 듣고 모르는 단어를 적어 둡니다.
- 0.75배속으로 한 문장씩 섀도잉을 시작하고, 익숙해지면 보통 속도로 돌아갑니다.
- 자신의 목소리를 녹음해 원본과 비교하고, recognition, detection, algorithm 같은 단어에 특히 주의합니다.
쉐도잉이란? 영어 실력을 빠르게 키우는 과학적 방법
쉐도잉(Shadowing)은 원래 전문 통역사 훈련을 위해 개발된 언어 학습 기법으로, 다언어 학자인 Dr. Alexander Arguelles에 의해 대중화된 방법입니다. 핵심 원리는 간단하지만 매우 강력합니다: 원어민의 영어를 들으면서 1~2초의 짧은 지연으로 즉시 소리 내어 따라 말하는 것——마치 '그림자(shadow)'처럼 화자를 따라가는 것입니다. 문법 공부나 수동적인 청취와 달리, 쉐도잉은 뇌와 입 근육이 동시에 실시간으로 영어를 처리하고 재현하도록 훈련합니다. 연구에 따르면 이 방법은 발음 정확도, 억양, 리듬, 연음, 청취력, 말하기 유창성을 크게 향상시킵니다. IELTS 스피킹 준비와 자연스러운 영어 소통을 원하는 분들에게 특히 효과적입니다.