跟读练习: C4W1L01 Computer Vision - 通过视频学习英语口语
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62 句
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Welcome to this course on convolutional networks.
欢迎学习这门关于卷积网络的课程。
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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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本课的词汇与口语要点
这段视频共有 62 个句子、981 个单词可供跟读。 讲话部分时长为 5:44。 说话人语速自然，每分钟约 171 个词，接近日常对话。 86% 的单词属于英语最常用的 3,000 词，其余的词建议在练习前先学一下。
视频中的重点词汇
视频中 15 个值得学习的单词，附发音和释义：
| 单词 | 发音 | 释义 |
|---|---|---|
| input 名词 | /ˈɪnpʊt/ | 輸入 /输入 |
| recognition 名词 | /ˌɹɛkəɡˈnɪʃ(ə)n/ | 認出 /认出 |
| algorithm 名词 | /ˈælɡəɹɪðm̩/ | 算法, 演算法 |
| parameter 名词 | /pəˈɹæm.ə.tɚ/ | 參數 /参数, 參量 /参量 |
| architecture 名词 | /ˈɑː.kɪˌtɛk.t͡ʃə/ | 建築 /建筑, 建築學 /建筑学 |
| requirement 名词 | /ɹɪˈkwaɪɹmənt/ | 要求, 需求 |
| implement 动词 | /ˈɪmpləmənt/ | 實施 /实施, 執行 /执行 |
| dimensional 形容词 | /daɪˈmɛn.ʃə.nəl/ | 維的 /维的 |
| dimension 名词 | /daɪˈmɛn.ʃən/ | 方面, 特點 /特点 |
| unlock 动词 | /ʌnˈlɑk/ | 開鎖 /开锁, 解鎖 /解锁 |
| pixel 名词 | /ˈpɪk.səl/ | 像素 |
| helpful 形容词 | /ˈhɛlp.fl̩/ | 有幫助 /有帮助 |
| layer 名词 | /ˈleɪ̯ɚ/ | 層 /层 |
| connect 动词 | /kəˈnɛkt/ | 連接 /连接 |
| rapid 形容词 | /ˈɹæp.ɪd/ | 快, 迅速 |
视频中出现的短语动词
| 单词 | 释义 |
|---|---|
| figure out 动词 | 弄清楚 |
| end up 动词 | 到达 |
| come up with 动词 | 想出, 提出 |
视频中的语法
说话人最常用的结构，并附上视频中的原话：
| 结构 | 视频中的用法 |
|---|---|
| 被动语态 be + 过去分词 — 强调发生了什么，而不是谁做的 | be created · be built · is actually inspired |
| 现在完成时 have/has + 过去分词 — 过去发生但与现在仍有关联的事 | has been · You've already seen · you've worked |
| 定语从句 who / which + 从句 — 补充说明人或事物 | parameters, which is · operation, which is |
需要注意的发音
说话人用了 13 次缩略和弱读形式，例如 don't, I'm, we'll。请按听到的简短形式来说。
- “sh” 和 “zh” 音: recognition /ˌɹɛkəɡˈnɪʃ(ə)n/, detection /dɪˈtɛk.ʃən/, dimensional /daɪˈmɛn.ʃə.nəl/, dimension /daɪˈmɛn.ʃən/, inspiration /ˌɪn.spɪˈɹeɪ.ʃən/
- 长单词——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/
如何用这段视频练习
- 先完整听一遍视频，不要开口，记下不认识的单词。
- 先用 0.75 倍速逐句跟读，熟练之后再回到正常速度。
- 录下自己的声音并与原声对比，特别注意 input, recognition, algorithm 这类单词。
什么是跟读法？
跟读法 (Shadowing) 是一种有科学依据的语言学习技巧，最初开发用于专业口译员的培训，并由多语言者Alexander Arguelles博士普及。这个方法简单而强大：您在听英语母语原声的同时立即大声重复——就像是一个延迟1-2秒紧跟说话者的影子。与被动听力或语法练习不同，跟读法强迫您的大脑和口腔肌肉同时处理并模仿真实的讲话模式。研究表明它能显着提高发音准确性，语调，节奏，连读，听力理解和口语流利度——使其成为雅思口语备考和真实英语交流最有效的方法之一。