Shadowing Practice: C4W1L01 Computer Vision - Learn English Speaking with Video
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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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Vocabulary and speaking notes for this lesson
This video has 62 sentences and 981 words to shadow. The speech runs for 5:44. The speaker talks at a natural 171 words per minute, close to everyday conversation. 86% of the words are among the 3,000 most common in English; the rest is worth studying before you start.
Key vocabulary in this video
15 words from the video worth learning, with pronunciation and meaning:
| Word | Pronunciation | Meaning |
|---|---|---|
| neural adjective | /ˈnʊɹəl/ | Of, or relating to the nerves, neurons or the nervous system. |
| input noun | /ˈɪnpʊt/ | The act or process of putting in; infusion. |
| matrix noun | /ˈmeɪ.tɹɪks/ | The cavity or mold in which anything is formed. |
| recognition noun | /ˌɹɛkəɡˈnɪʃ(ə)n/ | The act of recognizing or the condition of being recognized (matching a current observation with a memory of a prior observation of the same entity). |
| enable verb | /ɪˈneɪ.bəl/ | To make somebody able (to do, or to be, something); to give sufficient ability or power to do or to be; to give strength or ability to. |
| detection noun | /dɪˈtɛk.ʃən/ | The act or process of detecting, uncovering, or finding out, the discovery of something new, hidden, or disguised. |
| algorithm noun | /ˈælɡəɹɪðm̩/ | A collection of ordered steps that solve a mathematical problem. A precise step-by-step plan for a computational procedure that possibly begins with an input… |
| parameter noun | /pəˈɹæm.ə.tɚ/ | A value kept constant during an experiment, equation, calculation, or similar, but varied over other versions of the experiment, equation, calculation, etc. |
| architecture noun | /ˈɑː.kɪˌtɛk.t͡ʃə/ | The art and science of designing and managing the construction of buildings and other structures, particularly if they are well proportioned and decorated. |
| requirement noun | /ɹɪˈkwaɪɹmənt/ | A necessity or prerequisite; something required or obligatory. Its adpositions are generally of in relation to who or what has given it, on in relation to whom… |
| implement verb | /ˈɪmpləmənt/ | To bring about; to put into practice; to carry out. |
| dimensional adjective | /daɪˈmɛn.ʃə.nəl/ | Of or pertaining to dimensions. |
| dimension noun | /daɪˈmɛn.ʃən/ | A single aspect of a given thing. |
| unlock verb | /ʌnˈlɑk/ | To undo or open a lock or something locked by, for example, turning a key, or selecting a combination. |
| pixel noun | /ˈpɪk.səl/ | One of the tiny dots that make up the representation of an image in a computer's memory. |
Phrasal verbs you will hear
| Word | Meaning |
|---|---|
| figure out verb | To come to understand; to discover or find a solution; to deduce. |
| end up verb | To bring to a conclusion. |
| come up with verb | To manage to produce, deliver, or present (something) by inventing, creating, thinking of, or obtaining it. |
Grammar in this video
The structures the speaker uses most, with the exact words from the video:
| Structure | In the video |
|---|---|
| Passive voice be + past participle — the focus is on what happens, not who does it | be created · be built · is actually inspired |
| Present perfect have/has + past participle — a past action that still matters now | has been · You've already seen · you've worked |
| Relative clauses who / which + clause — extra information about a person or thing | parameters, which is · operation, which is |
Pronunciation to watch
The speaker uses 13 contractions and reduced forms, such as don't, I'm, we'll. Say them the short way, as you hear them.
- The “sh” and “zh” sounds: recognition /ˌɹɛkəɡˈnɪʃ(ə)n/, detection /dɪˈtɛk.ʃən/, dimensional /daɪˈmɛn.ʃə.nəl/, dimension /daɪˈmɛn.ʃən/, inspiration /ˌɪn.spɪˈɹeɪ.ʃən/
- Long words — get the stress right: recognition /ˌɹɛkəɡˈnɪʃ(ə)n/, parameter /pəˈɹæm.ə.tɚ/, architecture /ˈɑː.kɪˌtɛk.t͡ʃə/, dimensional /daɪˈmɛn.ʃə.nəl/, inspiration /ˌɪn.spɪˈɹeɪ.ʃən/
How to practise with this video
- Listen to the whole video once without speaking and note the words you do not know.
- Start at 0.75× speed, shadow it sentence by sentence, then go back to normal speed once it feels easy.
- Record yourself and compare with the original, paying attention to words like neural, input, matrix.
What is the Shadowing Technique?
Shadowing is a science-backed language learning technique originally developed for professional interpreter training and popularized by polyglot Dr. Alexander Arguelles. The method is simple but powerful: you listen to native English audio and immediately repeat it out loud — like a shadow following the speaker with just a 1–2 second delay. Unlike passive listening or grammar drills, shadowing forces your brain and mouth muscles to simultaneously process and reproduce real speech patterns. Research shows it significantly improves pronunciation accuracy, intonation, rhythm, connected speech, listening comprehension, and speaking fluency — making it one of the most effective methods for IELTS Speaking preparation and real-world English communication.