跟讀練習: C4W1L01 Computer Vision - 通過影片學習英語口語

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Welcome to this course on convolutional networks.

歡迎學習這門關於卷積網路的課程。

Computer vision is one of the areas that's been advancing rapidly thanks to deep learning.

Deep learning computer vision is now helping self-driving cars figure out where are the other cars

and the pedestrians around it so it's avoid them.

It's making face recognition work much better than ever before so

that perhaps some of you will soon or perhaps already be able to unlock a phone, unlock even a door using just your face.

And if you look on your cell phone, I bet you have many apps that show you pictures of food

or pictures of a hotel or just fun pictures of scenery.

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.

And I think deep learning is even enabling new types of art to be created.

So I think the two reasons I'm excited about deep learning for computer vision and why I think you might be too.

First, rapid advances in computer vision are enabling brand new applications to be built that just weren't possible a few years ago.

And by learning these tools, perhaps you will be able to invent some of these new products and applications.

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

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.

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.

So even if you don't end up working on computer vision, I hope

that you find some of the ideas you learned about in this course helpful for some of your algorithms and your architectures.

So with that, let's get started.

Here are some examples of computer vision problems we'll study in this course.

You've already seen image classification, sometimes also called image recognition, where you might take as input,

say a 64 by 64 image and try to figure out is that a cat.

Another example of a computer vision problem is object detection.

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,

but instead you need to figure out the position of the other cars in this picture

so that your car can avoid them.

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.

We'll have some other way of recognizing where in the picture are these objects.

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.

Here's another example, maybe a more fun one is neural style transfer.

Let's say you have a picture and you want this picture repainted in a different style.

So, neural style transfer, you have a content image and you have a style image.

The image on the right is actually a Picasso.

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,

and you end up with the image at the bottom.

So albums like this are enabling new types of artwork to be created.

And in this course, you learn how to do this yourself as well.

One of the challenges of computer vision problems is that the inputs can get really big.

For example, in previous courses, you've worked with 64 by 64 images.

And so that's 64 by 64 by 3 because there's three color channels.

And if you multiply that out, that's 12288.

So X, the input features has dimension 12288.

And that's not too bad.

But 64 by 64 is actually a very small image.

If you work with larger images, maybe this is a thousand pixel by thousand pixel image, and that's actually just one megapixel,

but the dimension of the input features will be thousand by thousand by three because you have three RGB channels,

and that's three million.

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.

But if you have 3 million input features, then this means that x here will be 3 million dimensional.

And so if in the first hidden layer, maybe you have just a thousand hidden units,

then the total number of weights that is the matrix W1,

if you use a standard fully connected network like we have in causes one or two,

this matrix will be a 1000 by 3 million dimensional matrix.

Because X is now R by 3 million, 3M, I'm using it to denote 3 million.

And this means that this matrix here will have 3 billion parameters, which is just very, very large.

And with that many parameters, it's difficult to get enough data to prevent the neural network from overfitting,

and also the computational requirements and memory requirements to train a neural network with three billion parameters is just a bit infeasible.

But for computer vision applications, you don't want to be stuck using only tiny little images.

You want to use large images.

To do that, you need to implement the convolution operation, which is one of the fundamental building blocks of convolutional neural networks.

Let's see what this means and how you can implement this in the next video.

I will illustrate convolutions using the example of edge detection.

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本課的詞彙與口語要點

這段影片共有 62 個句子、981 個單詞可供跟讀。講話部分時長為 5:44。說話人語速自然，每分鐘約 171 個詞，接近日常對話。 86% 的單詞屬於英語最常用的 3,000 詞，其餘的詞建議在練習前先學一下。

影片中的重點詞彙

影片中 15 個值得學習的單詞，附發音和釋義：

單詞	發音	釋義
input 名詞	/ˈɪnpʊt/	輸入 /輸入
recognition 名詞	/ˌɹɛkəɡˈnɪʃ(ə)n/	認出 /認出
algorithm 名詞	/ˈælɡəɹɪðm̩/	演算法, 演算法
parameter 名詞	/pəˈɹæm.ə.tɚ/	引數 /引數, 參量 /參量
architecture 名詞	/ˈɑː.kɪˌtɛk.t͡ʃə/	建築 /建築, 建築學 /建築學
requirement 名詞	/ɹɪˈkwaɪɹmənt/	要求, 需求
implement 動詞	/ˈɪmpləmənt/	實施 /實施, 執行 /執行
dimensional 形容詞	/daɪˈmɛn.ʃə.nəl/	維的 /維的
dimension 名詞	/daɪˈmɛn.ʃən/	方面, 特點 /特點
unlock 動詞	/ʌnˈlɑk/	開鎖 /開鎖, 解鎖 /解鎖
pixel 名詞	/ˈpɪk.səl/	畫素
helpful 形容詞	/ˈhɛlp.fl̩/	有幫助 /有幫助
layer 名詞	/ˈleɪ̯ɚ/	層 /層
connect 動詞	/kəˈnɛkt/	連線 /連線
rapid 形容詞	/ˈɹæp.ɪd/	快, 迅速

影片中出現的短語動詞

單詞	釋義
figure out 動詞	弄清楚
end up 動詞	到達
come up with 動詞	想出, 提出

影片中的語法

說話人最常用的結構，並附上影片中的原話：

結構	影片中的用法
被動語態 be + 過去分詞 — 強調發生了什麼，而不是誰做的	be created · be built · is actually inspired
現在完成時 have/has + 過去分詞 — 過去發生但與現在仍有關聯的事	has been · You've already seen · you've worked
定語從句 who / which + 從句 — 補充說明人或事物	parameters, which is · operation, which is

需要注意的發音

說話人用了 13 次縮略和弱讀形式，例如 don't, I'm, we'll。請按聽到的簡短形式來說。

“sh” 和 “zh” 音: recognition /ˌɹɛkəɡˈnɪʃ(ə)n/, detection /dɪˈtɛk.ʃən/, dimensional /daɪˈmɛn.ʃə.nəl/, dimension /daɪˈmɛn.ʃən/, inspiration /ˌɪn.spɪˈɹeɪ.ʃən/
長單詞——注意重音位置: recognition /ˌɹɛkəɡˈnɪʃ(ə)n/, parameter /pəˈɹæm.ə.tɚ/, architecture /ˈɑː.kɪˌtɛk.t͡ʃə/, dimensional /daɪˈmɛn.ʃə.nəl/, inspiration /ˌɪn.spɪˈɹeɪ.ʃən/

如何用這段影片練習

先完整聽一遍影片，不要開口，記下不認識的單詞。
先用 0.75 倍速逐句跟讀，熟練之後再回到正常速度。
錄下自己的聲音並與原聲對比，特別注意 input, recognition, algorithm 這類單詞。

什麼是跟讀法？

跟讀法 (Shadowing) 是一種有科學依據的語言學習技巧，最初開發用於專業口譯員的培訓，並由多語言者Alexander Arguelles博士普及。這個方法簡單而強大：您在聽英語母語原聲的同時立即大聲重複——就像是一個延遲1-2秒緊跟說話者的影子。與被動聽力或語法練習不同，跟讀法強迫您的大腦和口腔肌肉同時處理並模模擬實的講話模式。研究表明它能顯著提高發音準確性，語調，節奏，連讀，聽力理解和口語流利度——使其成為雅思口語備考和真實英語交流最有效的方法之一。

影子跟讀法: 閱讀完整分步指南 →

跟讀練習: C4W1L01 Computer Vision - 通過影片學習英語口語

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