シャドーイング練習: C4W1L01 Computer Vision - 動画で英語スピーキングを学ぶ
練習モードを選ぶ
各モードは全画面の練習ルームで開きます。
62 文
文が短すぎたり長すぎる場合は、Editをタップして調整してください。
1
Welcome to this course on convolutional networks.
2
Computer vision is one of the areas that's been advancing rapidly thanks to deep learning.
3
Deep learning computer vision is now helping self-driving cars figure out where are the other cars
4
and the pedestrians around it so it's avoid them.
5
It's making face recognition work much better than ever before so
6
that perhaps some of you will soon or perhaps already be able to unlock a phone, unlock even a door using just your face.
7
And if you look on your cell phone, I bet you have many apps that show you pictures of food
8
or pictures of a hotel or just fun pictures of scenery.
9
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.
10
And I think deep learning is even enabling new types of art to be created.
11
So I think the two reasons I'm excited about deep learning for computer vision and why I think you might be too.
12
First, rapid advances in computer vision are enabling brand new applications to be built that just weren't possible a few years ago.
13
And by learning these tools, perhaps you will be able to invent some of these new products and applications.
14
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
15
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.
16
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.
17
So even if you don't end up working on computer vision, I hope
18
that you find some of the ideas you learned about in this course helpful for some of your algorithms and your architectures.
19
So with that, let's get started.
20
Here are some examples of computer vision problems we'll study in this course.
21
You've already seen image classification, sometimes also called image recognition, where you might take as input,
22
say a 64 by 64 image and try to figure out is that a cat.
23
Another example of a computer vision problem is object detection.
24
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,
25
but instead you need to figure out the position of the other cars in this picture
26
so that your car can avoid them.
27
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.
28
We'll have some other way of recognizing where in the picture are these objects.
29
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.
30
Here's another example, maybe a more fun one is neural style transfer.
31
Let's say you have a picture and you want this picture repainted in a different style.
32
So, neural style transfer, you have a content image and you have a style image.
33
The image on the right is actually a Picasso.
34
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,
35
and you end up with the image at the bottom.
36
So albums like this are enabling new types of artwork to be created.
37
And in this course, you learn how to do this yourself as well.
38
One of the challenges of computer vision problems is that the inputs can get really big.
39
For example, in previous courses, you've worked with 64 by 64 images.
40
And so that's 64 by 64 by 3 because there's three color channels.
41
And if you multiply that out, that's 12288.
42
So X, the input features has dimension 12288.
43
And that's not too bad.
44
But 64 by 64 is actually a very small image.
45
If you work with larger images, maybe this is a thousand pixel by thousand pixel image, and that's actually just one megapixel,
46
but the dimension of the input features will be thousand by thousand by three because you have three RGB channels,
47
and that's three million.
48
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.
49
But if you have 3 million input features, then this means that x here will be 3 million dimensional.
50
And so if in the first hidden layer, maybe you have just a thousand hidden units,
51
then the total number of weights that is the matrix W1,
52
if you use a standard fully connected network like we have in causes one or two,
53
this matrix will be a 1000 by 3 million dimensional matrix.
54
Because X is now R by 3 million, 3M, I'm using it to denote 3 million.
55
And this means that this matrix here will have 3 billion parameters, which is just very, very large.
56
And with that many parameters, it's difficult to get enough data to prevent the neural network from overfitting,
57
and also the computational requirements and memory requirements to train a neural network with three billion parameters is just a bit infeasible.
58
But for computer vision applications, you don't want to be stuck using only tiny little images.
59
You want to use large images.
60
To do that, you need to implement the convolution operation, which is one of the fundamental building blocks of convolutional neural networks.
61
Let's see what this means and how you can implement this in the next video.
62
I will illustrate convolutions using the example of edge detection.
アプリをダウンロード
流暢に話すために必要なすべて
AI発音採点文ごとに採点
IPA練習すべての音をマスター
単語学習自分の単語帳を作る
単語ゲーム遊びながら覚える
📺 同じチャンネル
![#1 Machine Learning Specialization [Course 1, Week 1, Lesson 1]](https://img.youtube.com/vi/vStJoetOxJg/mqdefault.jpg)
✨ おすすめ動画
C13
43 文
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!)】
Business EnglishUniversity StudentsWorking Adults
C12
106 文
What is Engineering?: Crash Course Engineering #1
Business EnglishUniversity StudentsWorking Adults
C14
123 文
Introduction to Diminsionality Reduction (a.k.a. Manifold Learning)
Business EnglishUniversity StudentsWorking Adults
C117
118 文
Computational Linguistics: Crash Course Linguistics #15
Business EnglishUniversity StudentsWorking Adults
C13
51 文
Business Partner C1. Unit 1. Video 1.1.1.
Business EnglishUniversity StudentsWorking Adults
C19
38 文
The Neuroscience of Learning
Business EnglishUniversity StudentsWorking Adults
C14
119 文
IELTS Listening Practice B2–C1 | The Importance of Lifelong Learning in Modern Society
IELTS PrepBusiness EnglishUniversity Students
C11
12 文
Rapid Engineering With AI
Business EnglishUniversity StudentsWorking Adults
C13
208 文
AI & Education: Generative AI & the Future of Critical Thinking
Business EnglishUniversity StudentsWorking Adults
C16
10 文
What's new in Google Cloud in less than 1 minute
Business EnglishUniversity StudentsWorking Adults
C16
27 文
AI breaks barriers for the hearing impaired | Vietnam Today
Business EnglishUniversity StudentsWorking Adults
C18
102 文
World Models explained in 10min..
Business EnglishUniversity StudentsWorking Adults
このレッスンの語彙とスピーキングのポイント
この動画には、シャドーイング用の文が62文、単語が981語あります。 音声の長さは5:44です。 話す速さは1分あたり約171語で、日常会話に近い自然なペースです。 単語の86%は英語の頻出3,000語に含まれます。残りは練習の前に確認しておきましょう。
この動画の重要語彙
動画に出てくる覚えておきたい単語15語を、発音と意味つきで紹介します。
| 単語 | 発音 | 意味 |
|---|---|---|
| recognition 名詞 | /ˌɹɛkəɡˈnɪʃ(ə)n/ | 承認 |
| algorithm 名詞 | /ˈælɡəɹɪðm̩/ | アルゴリズム, 演算手順 |
| parameter 名詞 | /pəˈɹæm.ə.tɚ/ | 媒介変数, パラメーター |
| architecture 名詞 | /ˈɑː.kɪˌtɛk.t͡ʃə/ | 建築, 建築学 |
| requirement 名詞 | /ɹɪˈkwaɪɹmənt/ | 要求 |
| implement 動詞 | /ˈɪmpləmənt/ | 実行する, 実現する |
| dimensional 形容詞 | /daɪˈmɛn.ʃə.nəl/ | -次元の |
| dimension 名詞 | /daɪˈmɛn.ʃən/ | 面積, 寸法 |
| unlock 動詞 | /ʌnˈlɑk/ | 開ける, 解錠する |
| pixel 名詞 | /ˈpɪk.səl/ | ピクセル, 画素 |
| helpful 形容詞 | /ˈhɛlp.fl̩/ | 役に立つ, 有用 |
| layer 名詞 | /ˈleɪ̯ɚ/ | 層, 膜 |
| connect 動詞 | /kəˈnɛkt/ | 繋げる |
| rapid 形容詞 | /ˈɹæp.ɪd/ | 速い, 急な |
| fundamental 形容詞 | /ˌfʌn.dəˈmɛn.təl/ | 基本的な |
この動画の文法
話し手がよく使っている文型を、動画の実際の表現とともに紹介します。
| 文型 | 動画での表現 |
|---|---|
| 受動態 be + 過去分詞 — 誰がするかより、何が起きるかに焦点を当てる | be created · be built · is actually inspired |
| 現在完了形 have/has + 過去分詞 — 過去の出来事が今も関係している | has been · You've already seen · you've worked |
| 関係詞節 who / which + 節 — 人や物について情報を加える | parameters, which is · operation, which is |
注意したい発音
話し手はdon't, I'm, we'llなど、短縮形や弱形を13回使っています。聞こえたとおりの短い形で発音しましょう。
- 「sh」と「zh」の音: recognition /ˌɹɛkəɡˈnɪʃ(ə)n/, detection /dɪˈtɛk.ʃən/, dimensional /daɪˈmɛn.ʃə.nəl/, dimension /daɪˈmɛn.ʃən/, inspiration /ˌɪn.spɪˈɹeɪ.ʃən/
- 長い単語（アクセントの位置に注意）: recognition /ˌɹɛkəɡˈnɪʃ(ə)n/, parameter /pəˈɹæm.ə.tɚ/, architecture /ˈɑː.kɪˌtɛk.t͡ʃə/, dimensional /daɪˈmɛn.ʃə.nəl/, inspiration /ˌɪn.spɪˈɹeɪ.ʃən/
この動画での練習方法
- まず声を出さずに動画を最後まで聞き、知らない単語をメモします。
- まず0.75倍速で一文ずつシャドーイングし、慣れてきたら通常の速度に戻します。
- 自分の声を録音して元の音声と比べます。recognition, algorithm, parameterなどの単語に特に注意しましょう。
シャドーイングとは？英語上達に効果的な理由
シャドーイング（Shadowing）は、もともとプロの通訳者養成プログラムで開発された言語学習法で、多言語習得者として知られるDr. Alexander Arguelles によって広く普及されました。方法はシンプルですが非常に効果的：ネイティブスピーカーの英語を聞きながら、1〜2秒の遅延で声に出してすぐに繰り返す——まるで「影（shadow）」のように話者を追いかけます。文法ドリルや受動的なリスニングと異なり、シャドーイングは脳と口の筋肉が同時にリアルタイムで英語を処理・再現することを強制します。研究により、発音精度、抑揚、リズム、連音、リスニング力、そして会話の流暢さが大幅に向上することが確認されています。IELTSスピーキング対策や自然な英語コミュニケーションを目指す方に特におすすめです。