تدريب Shadowing: C4W1L01 Computer Vision - تعلم التحدث بالإنجليزية عبر الفيديو
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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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المفرداتابنِ رصيدك من الكلمات
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المفردات وملاحظات التحدث لهذا الدرس
يحتوي هذا الفيديو على 62 جملة و981 كلمة للتدرب على الترديد. مدة الكلام 5:44. يتكلم المتحدث بسرعة طبيعية، نحو 171 كلمة في الدقيقة، قريبة من المحادثة اليومية. 86% من الكلمات ضمن أكثر 3,000 كلمة شيوعًا في الإنجليزية، ويُستحسن مراجعة الباقي قبل البدء.
أهم المفردات في هذا الفيديو
15 كلمة من الفيديو تستحق التعلّم، مع النطق والمعنى:
| الكلمة | النطق | المعنى |
|---|---|---|
| algorithm اسم | /ˈælɡəɹɪðm̩/ | خَوَارِزْمِيَّة |
| parameter اسم | /pəˈɹæm.ə.tɚ/ | وَسِيط, بَارَامِتِر |
| architecture اسم | /ˈɑː.kɪˌtɛk.t͡ʃə/ | هَنْدَسَة مِعْمَارِيَّة, عِمَارَة |
| requirement اسم | /ɹɪˈkwaɪɹmənt/ | مَطْلَب, طَلَب |
| implement فعل | /ˈɪmpləmənt/ | نَفَّذَ, نَجَزَ |
| dimension اسم | /daɪˈmɛn.ʃən/ | بُعْد |
| pixel اسم | /ˈpɪk.səl/ | بِكْسِل |
| helpful صفة | /ˈhɛlp.fl̩/ | نَافِع, مُفِيد |
| inspiration اسم | /ˌɪn.spɪˈɹeɪ.ʃən/ | إِلْهَام, وَحْي |
| layer اسم | /ˈleɪ̯ɚ/ | طَبَقَة |
| connect فعل | /kəˈnɛkt/ | رَبَطَ |
| rapid صفة | /ˈɹæp.ɪd/ | سَرِيع |
| fundamental صفة | /ˌfʌn.dəˈmɛn.təl/ | أَسَاسِيّ |
| classification اسم | /ˌklæsɪfɪˈkeɪʃən/ | تَصْنِيف |
| borrow فعل | /ˈbɑɹ.oʊ/ | اِسْتَعَارَ |
الأفعال المركبة التي ستسمعها
| الكلمة | المعنى |
|---|---|
| come up with فعل | جَاءَ بِـ, أَتَى بِـ |
القواعد في هذا الفيديو
أكثر التراكيب التي يستخدمها المتحدث، مع العبارات نفسها من الفيديو:
| التركيب | في الفيديو |
|---|---|
| المبني للمجهول be + اسم المفعول — التركيز على ما يحدث لا على من يفعله | be created · be built · is actually inspired |
| المضارع التام have/has + اسم المفعول — حدث ماضٍ ما زال له أثر الآن | has been · You've already seen · you've worked |
| جمل الوصل who / which + جملة — معلومة إضافية عن شخص أو شيء | parameters, which is · operation, which is |
نطق يحتاج إلى انتباه
يستخدم المتحدث 13 من الصيغ المختصرة، مثل don't, I'm, we'll. انطقها بصيغتها القصيرة كما تسمعها.
- أصوات “sh” و“zh”: recognition /ˌɹɛkəɡˈnɪʃ(ə)n/, detection /dɪˈtɛk.ʃən/, dimensional /daɪˈmɛn.ʃə.nəl/, dimension /daɪˈmɛn.ʃən/, inspiration /ˌɪn.spɪˈɹeɪ.ʃən/
- الكلمات الطويلة — انتبه لموضع النبر: recognition /ˌɹɛkəɡˈnɪʃ(ə)n/, parameter /pəˈɹæm.ə.tɚ/, architecture /ˈɑː.kɪˌtɛk.t͡ʃə/, dimensional /daɪˈmɛn.ʃə.nəl/, inspiration /ˌɪn.spɪˈɹeɪ.ʃən/
كيف تتدرب بهذا الفيديو
- استمع إلى الفيديو كاملًا مرة واحدة دون أن تتكلم، ودوّن الكلمات التي لا تعرفها.
- ابدأ بسرعة 0.75×، وردّد جملة جملة، ثم عُد إلى السرعة العادية عندما يصبح الأمر سهلًا.
- سجّل صوتك وقارنه بالأصل، مع الانتباه إلى كلمات مثل algorithm, parameter, architecture.
ما هي تقنية التظليل الصوتي؟
التظليل الصوتي (Shadowing) تقنية تعلم لغة مدعومة علمياً، طُورت أصلاً لتدريب المترجمين الفوريين المحترفين. الطريقة بسيطة لكنها قوية: تستمع لصوت إنجليزي أصلي وتكرره فوراً بصوت عالٍ — كظل يتبع المتحدث بتأخير 1-2 ثانية. تُظهر الأبحاث تحسناً كبيراً في دقة النطق والتنغيم والإيقاع وربط الأصوات والاستماع والطلاقة.